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Google Machine Learning Glossary

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Google Machine Learning Glossary

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1. Synthetic Feature

A feature that is not present among the input features - but is derived from one or more of them. Kinds of synthetic features include the following: Multiplying one feature by itself or by other feature(s). (These are termed feature crosses.) - Dividing one feature by a second feature. Bucketing a continuous feature into range bins. Features created by normalizing or scaling alone are not considered synthetic features.Synonym for loss.Training a model to find patterns in a data set - typically an unlabeled data set. The most common use of unsupervised machine learning is to cluster data into groups of similar examples. For example - an unsupervised machine learning algorithm can cluster songs together based on various properties of the music. The resulting clusters can become an input to other machine learning algorithms (for example - to a music recommendation service). Clustering can be helpful in domains where true labels are hard to obtain. For example - in domains such as anti-abuse and fraud - clusters can help humans better understand the data. Another example of unsupervised machine learning is principal component analysis (PCA). For example - applying PCA on a data set containing the contents of millions of shopping carts might reveal that shopping carts containing lemons frequently also contain antacids. Compare with supervised machine learning.Synonym for learning rate.

2. Cost

Training a model to find patterns in a data set - typically an unlabeled data set. The most common use of unsupervised machine learning is to cluster data into groups of similar examples. For example - an unsupervised machine learning algorithm can cluster songs together based on various properties of the music. The resulting clusters can become an input to other machine learning algorithms (for example - to a music recommendation service). Clustering can be helpful in domains where true labels are hard to obtain. For example - in domains such as anti-abuse and fraud - clusters can help humans better understand the data. Another example of unsupervised machine learning is principal component analysis (PCA). For example - applying PCA on a data set containing the contents of millions of shopping carts might reveal that shopping carts containing lemons frequently also contain antacids. Compare with supervised machine learning.Synonym for learning rate.A feature that is not present among the input features - but is derived from one or more of them. Kinds of synthetic features include the following: Multiplying one feature by itself or by other feature(s). (These are termed feature crosses.) - Dividing one feature by a second feature. Bucketing a continuous feature into range bins. Features created by normalizing or scaling alone are not considered synthetic features.Synonym for loss.

3. Unsupervised Machine Learning

A feature that is not present among the input features - but is derived from one or more of them. Kinds of synthetic features include the following: Multiplying one feature by itself or by other feature(s). (These are termed feature crosses.) - Dividing one feature by a second feature. Bucketing a continuous feature into range bins. Features created by normalizing or scaling alone are not considered synthetic features.Synonym for learning rate.Synonym for loss.Training a model to find patterns in a data set - typically an unlabeled data set. The most common use of unsupervised machine learning is to cluster data into groups of similar examples. For example - an unsupervised machine learning algorithm can cluster songs together based on various properties of the music. The resulting clusters can become an input to other machine learning algorithms (for example - to a music recommendation service). Clustering can be helpful in domains where true labels are hard to obtain. For example - in domains such as anti-abuse and fraud - clusters can help humans better understand the data. Another example of unsupervised machine learning is principal component analysis (PCA). For example - applying PCA on a data set containing the contents of millions of shopping carts might reveal that shopping carts containing lemons frequently also contain antacids. Compare with supervised machine learning.

4. Step Size

Synonym for loss.Synonym for learning rate.Training a model to find patterns in a data set - typically an unlabeled data set. The most common use of unsupervised machine learning is to cluster data into groups of similar examples. For example - an unsupervised machine learning algorithm can cluster songs together based on various properties of the music. The resulting clusters can become an input to other machine learning algorithms (for example - to a music recommendation service). Clustering can be helpful in domains where true labels are hard to obtain. For example - in domains such as anti-abuse and fraud - clusters can help humans better understand the data. Another example of unsupervised machine learning is principal component analysis (PCA). For example - applying PCA on a data set containing the contents of millions of shopping carts might reveal that shopping carts containing lemons frequently also contain antacids. Compare with supervised machine learning.A feature that is not present among the input features - but is derived from one or more of them. Kinds of synthetic features include the following: Multiplying one feature by itself or by other feature(s). (These are termed feature crosses.) - Dividing one feature by a second feature. Bucketing a continuous feature into range bins. Features created by normalizing or scaling alone are not considered synthetic features.

5. Binning

Synonym for loss.Training a model to find patterns in a data set - typically an unlabeled data set. The most common use of unsupervised machine learning is to cluster data into groups of similar examples. For example - an unsupervised machine learning algorithm can cluster songs together based on various properties of the music. The resulting clusters can become an input to other machine learning algorithms (for example - to a music recommendation service). Clustering can be helpful in domains where true labels are hard to obtain. For example - in domains such as anti-abuse and fraud - clusters can help humans better understand the data. Another example of unsupervised machine learning is principal component analysis (PCA). For example - applying PCA on a data set containing the contents of millions of shopping carts might reveal that shopping carts containing lemons frequently also contain antacids. Compare with supervised machine learning.See bucketing.A feature that is not present among the input features - but is derived from one or more of them. Kinds of synthetic features include the following: Multiplying one feature by itself or by other feature(s). (These are termed feature crosses.) - Dividing one feature by a second feature. Bucketing a continuous feature into range bins. Features created by normalizing or scaling alone are not considered synthetic features.

6. Test Set

Synonym for loss.A feature that is not present among the input features - but is derived from one or more of them. Kinds of synthetic features include the following: Multiplying one feature by itself or by other feature(s). (These are termed feature crosses.) - Dividing one feature by a second feature. Bucketing a continuous feature into range bins. Features created by normalizing or scaling alone are not considered synthetic features.The subset of the data set that you use to test your model after the model has gone through initial vetting by the validation set. Contrast with training set and validation set.Training a model to find patterns in a data set - typically an unlabeled data set. The most common use of unsupervised machine learning is to cluster data into groups of similar examples. For example - an unsupervised machine learning algorithm can cluster songs together based on various properties of the music. The resulting clusters can become an input to other machine learning algorithms (for example - to a music recommendation service). Clustering can be helpful in domains where true labels are hard to obtain. For example - in domains such as anti-abuse and fraud - clusters can help humans better understand the data. Another example of unsupervised machine learning is principal component analysis (PCA). For example - applying PCA on a data set containing the contents of millions of shopping carts might reveal that shopping carts containing lemons frequently also contain antacids. Compare with supervised machine learning.

7. False Negative (FN)

A feature that is not present among the input features - but is derived from one or more of them. Kinds of synthetic features include the following: Multiplying one feature by itself or by other feature(s). (These are termed feature crosses.) - Dividing one feature by a second feature. Bucketing a continuous feature into range bins. Features created by normalizing or scaling alone are not considered synthetic features.Training a model to find patterns in a data set - typically an unlabeled data set. The most common use of unsupervised machine learning is to cluster data into groups of similar examples. For example - an unsupervised machine learning algorithm can cluster songs together based on various properties of the music. The resulting clusters can become an input to other machine learning algorithms (for example - to a music recommendation service). Clustering can be helpful in domains where true labels are hard to obtain. For example - in domains such as anti-abuse and fraud - clusters can help humans better understand the data. Another example of unsupervised machine learning is principal component analysis (PCA). For example - applying PCA on a data set containing the contents of millions of shopping carts might reveal that shopping carts containing lemons frequently also contain antacids. Compare with supervised machine learning.Synonym for loss.An example in which the model mistakenly predicted the negative class. For example - the model inferred that a particular email message was not spam (the negative class) - but that email message actually was spam.

8. Regularization

Synonym for loss.Training a model to find patterns in a data set - typically an unlabeled data set. The most common use of unsupervised machine learning is to cluster data into groups of similar examples. For example - an unsupervised machine learning algorithm can cluster songs together based on various properties of the music. The resulting clusters can become an input to other machine learning algorithms (for example - to a music recommendation service). Clustering can be helpful in domains where true labels are hard to obtain. For example - in domains such as anti-abuse and fraud - clusters can help humans better understand the data. Another example of unsupervised machine learning is principal component analysis (PCA). For example - applying PCA on a data set containing the contents of millions of shopping carts might reveal that shopping carts containing lemons frequently also contain antacids. Compare with supervised machine learning.A feature that is not present among the input features - but is derived from one or more of them. Kinds of synthetic features include the following: Multiplying one feature by itself or by other feature(s). (These are termed feature crosses.) - Dividing one feature by a second feature. Bucketing a continuous feature into range bins. Features created by normalizing or scaling alone are not considered synthetic features.The penalty on a model's complexity. Regularization helps prevent overfitting. Different kinds of regularization include: L1 regularization - L2 regularization - dropout regularization - early stopping (this is not a formal regularization method - but can effectively limit overfitting)

9. Sigmoid Function

Synonym for loss.Training a model to find patterns in a data set - typically an unlabeled data set. The most common use of unsupervised machine learning is to cluster data into groups of similar examples. For example - an unsupervised machine learning algorithm can cluster songs together based on various properties of the music. The resulting clusters can become an input to other machine learning algorithms (for example - to a music recommendation service). Clustering can be helpful in domains where true labels are hard to obtain. For example - in domains such as anti-abuse and fraud - clusters can help humans better understand the data. Another example of unsupervised machine learning is principal component analysis (PCA). For example - applying PCA on a data set containing the contents of millions of shopping carts might reveal that shopping carts containing lemons frequently also contain antacids. Compare with supervised machine learning.A feature that is not present among the input features - but is derived from one or more of them. Kinds of synthetic features include the following: Multiplying one feature by itself or by other feature(s). (These are termed feature crosses.) - Dividing one feature by a second feature. Bucketing a continuous feature into range bins. Features created by normalizing or scaling alone are not considered synthetic features.A function that maps logistic or multinomial regression output (log odds) to probabilities - returning a value between 0 and 1. The sigmoid function has the following formula: y=11+eâˆ’Ïƒ - where Ïƒ in logistic regression problems is simply: Ïƒ=b+w1x1+w2x2+...wnxn - In other words - the sigmoid function converts Ïƒ into a probability between 0 and 1. In some neural networks - the sigmoid function acts as the activation function.

10. Stochastic Gradient Descent (SGD)

Synonym for loss.A gradient descent algorithm in which the batch size is one. In other words - SGD relies on a single example chosen uniformly at random from a data set to calculate an estimate of the gradient at each step. Structural Risk Minimization (SRM) (a)algorithm that balances two goals: The desire to build the most predictive model (for example - lowest loss). The desire to keep the model as simple as possible (for example - strong regularization). For example - a model function that minimizes loss+regularization on the training set is a structural risk minimization algorithm. Contrast with empirical risk minimization.Training a model to find patterns in a data set - typically an unlabeled data set. The most common use of unsupervised machine learning is to cluster data into groups of similar examples. For example - an unsupervised machine learning algorithm can cluster songs together based on various properties of the music. The resulting clusters can become an input to other machine learning algorithms (for example - to a music recommendation service). Clustering can be helpful in domains where true labels are hard to obtain. For example - in domains such as anti-abuse and fraud - clusters can help humans better understand the data. Another example of unsupervised machine learning is principal component analysis (PCA). For example - applying PCA on a data set containing the contents of millions of shopping carts might reveal that shopping carts containing lemons frequently also contain antacids. Compare with supervised machine learning.A feature that is not present among the input features - but is derived from one or more of them. Kinds of synthetic features include the following: Multiplying one feature by itself or by other feature(s). (These are termed feature crosses.) - Dividing one feature by a second feature. Bucketing a continuous feature into range bins. Features created by normalizing or scaling alone are not considered synthetic features.

11. Precision

Synonym for loss.A feature that is not present among the input features - but is derived from one or more of them. Kinds of synthetic features include the following: Multiplying one feature by itself or by other feature(s). (These are termed feature crosses.) - Dividing one feature by a second feature. Bucketing a continuous feature into range bins. Features created by normalizing or scaling alone are not considered synthetic features.A metric for classification models. Precision identifies the frequency with which a model was correct when predicting the positive class. That is: Precision=True Positives/(True Positives+False Positives)Training a model to find patterns in a data set - typically an unlabeled data set. The most common use of unsupervised machine learning is to cluster data into groups of similar examples. For example - an unsupervised machine learning algorithm can cluster songs together based on various properties of the music. The resulting clusters can become an input to other machine learning algorithms (for example - to a music recommendation service). Clustering can be helpful in domains where true labels are hard to obtain. For example - in domains such as anti-abuse and fraud - clusters can help humans better understand the data. Another example of unsupervised machine learning is principal component analysis (PCA). For example - applying PCA on a data set containing the contents of millions of shopping carts might reveal that shopping carts containing lemons frequently also contain antacids. Compare with supervised machine learning.

12. SavedModel

A feature that is not present among the input features - but is derived from one or more of them. Kinds of synthetic features include the following: Multiplying one feature by itself or by other feature(s). (These are termed feature crosses.) - Dividing one feature by a second feature. Bucketing a continuous feature into range bins. Features created by normalizing or scaling alone are not considered synthetic features.Training a model to find patterns in a data set - typically an unlabeled data set. The most common use of unsupervised machine learning is to cluster data into groups of similar examples. For example - an unsupervised machine learning algorithm can cluster songs together based on various properties of the music. The resulting clusters can become an input to other machine learning algorithms (for example - to a music recommendation service). Clustering can be helpful in domains where true labels are hard to obtain. For example - in domains such as anti-abuse and fraud - clusters can help humans better understand the data. Another example of unsupervised machine learning is principal component analysis (PCA). For example - applying PCA on a data set containing the contents of millions of shopping carts might reveal that shopping carts containing lemons frequently also contain antacids. Compare with supervised machine learning.The recommended format for saving and recovering TensorFlow models. SavedModel is a language-neutral - recoverable serialization format - which enables higher-level systems and tools to produce - consume - and transform TensorFlow models. See Saving and Restoring in the TensorFlow Programmer's Guide for complete details.Synonym for loss.

13. Backpropagation

Training a model to find patterns in a data set - typically an unlabeled data set. The most common use of unsupervised machine learning is to cluster data into groups of similar examples. For example - an unsupervised machine learning algorithm can cluster songs together based on various properties of the music. The resulting clusters can become an input to other machine learning algorithms (for example - to a music recommendation service). Clustering can be helpful in domains where true labels are hard to obtain. For example - in domains such as anti-abuse and fraud - clusters can help humans better understand the data. Another example of unsupervised machine learning is principal component analysis (PCA). For example - applying PCA on a data set containing the contents of millions of shopping carts might reveal that shopping carts containing lemons frequently also contain antacids. Compare with supervised machine learning.The primary algorithm for performing gradient descent on neural networks. First - the output values of each node are calculated (and cached) in a forward pass. Then - the partial derivative of the error with respect to each parameter is calculated in a backward pass through the graph.Synonym for loss.A feature that is not present among the input features - but is derived from one or more of them. Kinds of synthetic features include the following: Multiplying one feature by itself or by other feature(s). (These are termed feature crosses.) - Dividing one feature by a second feature. Bucketing a continuous feature into range bins. Features created by normalizing or scaling alone are not considered synthetic features.

14. Categorical Data

Features having a discrete set of possible values. For example - consider a categorical feature named house style - which has a discrete set of three possible values: Tudor - ranch - colonial. By representing house style as categorical data - the model can learn the separate impacts of Tudor - ranch - and colonial on house price. Sometimes - values in the discrete set are mutually exclusive - and only one value can be applied to a given example. For example - a car maker categorical feature would probably permit only a single value (Toyota) per example. Other times - more than one value may be applicable. A single car could be painted more than one different color - so a car color categorical feature would likely permit a single example to have multiple values (for example - red and white). Categorical features are sometimes called discrete features. Contrast with numerical data.Synonym for loss.A feature that is not present among the input features - but is derived from one or more of them. Kinds of synthetic features include the following: Multiplying one feature by itself or by other feature(s). (These are termed feature crosses.) - Dividing one feature by a second feature. Bucketing a continuous feature into range bins. Features created by normalizing or scaling alone are not considered synthetic features.Training a model to find patterns in a data set - typically an unlabeled data set. The most common use of unsupervised machine learning is to cluster data into groups of similar examples. For example - an unsupervised machine learning algorithm can cluster songs together based on various properties of the music. The resulting clusters can become an input to other machine learning algorithms (for example - to a music recommendation service). Clustering can be helpful in domains where true labels are hard to obtain. For example - in domains such as anti-abuse and fraud - clusters can help humans better understand the data. Another example of unsupervised machine learning is principal component analysis (PCA). For example - applying PCA on a data set containing the contents of millions of shopping carts might reveal that shopping carts containing lemons frequently also contain antacids. Compare with supervised machine learning.

15. Training

Training a model to find patterns in a data set - typically an unlabeled data set. The most common use of unsupervised machine learning is to cluster data into groups of similar examples. For example - an unsupervised machine learning algorithm can cluster songs together based on various properties of the music. The resulting clusters can become an input to other machine learning algorithms (for example - to a music recommendation service). Clustering can be helpful in domains where true labels are hard to obtain. For example - in domains such as anti-abuse and fraud - clusters can help humans better understand the data. Another example of unsupervised machine learning is principal component analysis (PCA). For example - applying PCA on a data set containing the contents of millions of shopping carts might reveal that shopping carts containing lemons frequently also contain antacids. Compare with supervised machine learning.A feature that is not present among the input features - but is derived from one or more of them. Kinds of synthetic features include the following: Multiplying one feature by itself or by other feature(s). (These are termed feature crosses.) - Dividing one feature by a second feature. Bucketing a continuous feature into range bins. Features created by normalizing or scaling alone are not considered synthetic features.Synonym for loss.The process of determining the ideal parameters comprising a model.

16. Parameter Update

Synonym for loss.Training a model to find patterns in a data set - typically an unlabeled data set. The most common use of unsupervised machine learning is to cluster data into groups of similar examples. For example - an unsupervised machine learning algorithm can cluster songs together based on various properties of the music. The resulting clusters can become an input to other machine learning algorithms (for example - to a music recommendation service). Clustering can be helpful in domains where true labels are hard to obtain. For example - in domains such as anti-abuse and fraud - clusters can help humans better understand the data. Another example of unsupervised machine learning is principal component analysis (PCA). For example - applying PCA on a data set containing the contents of millions of shopping carts might reveal that shopping carts containing lemons frequently also contain antacids. Compare with supervised machine learning.A feature that is not present among the input features - but is derived from one or more of them. Kinds of synthetic features include the following: Multiplying one feature by itself or by other feature(s). (These are termed feature crosses.) - Dividing one feature by a second feature. Bucketing a continuous feature into range bins. Features created by normalizing or scaling alone are not considered synthetic features.The operation of adjusting a model's parameters during training - typically within a single iteration of gradient descent.

17. Tensor Rank

Synonym for loss.Training a model to find patterns in a data set - typically an unlabeled data set. The most common use of unsupervised machine learning is to cluster data into groups of similar examples. For example - an unsupervised machine learning algorithm can cluster songs together based on various properties of the music. The resulting clusters can become an input to other machine learning algorithms (for example - to a music recommendation service). Clustering can be helpful in domains where true labels are hard to obtain. For example - in domains such as anti-abuse and fraud - clusters can help humans better understand the data. Another example of unsupervised machine learning is principal component analysis (PCA). For example - applying PCA on a data set containing the contents of millions of shopping carts might reveal that shopping carts containing lemons frequently also contain antacids. Compare with supervised machine learning.A feature that is not present among the input features - but is derived from one or more of them. Kinds of synthetic features include the following: Multiplying one feature by itself or by other feature(s). (These are termed feature crosses.) - Dividing one feature by a second feature. Bucketing a continuous feature into range bins. Features created by normalizing or scaling alone are not considered synthetic features.See rank.

18. Interpretability

A feature that is not present among the input features - but is derived from one or more of them. Kinds of synthetic features include the following: Multiplying one feature by itself or by other feature(s). (These are termed feature crosses.) - Dividing one feature by a second feature. Bucketing a continuous feature into range bins. Features created by normalizing or scaling alone are not considered synthetic features.Synonym for loss.The degree to which a model's predictions can be readily explained. Deep models are often non-interpretable; that is - a deep model's different layers can be hard to decipher. By contrast - linear regression models and wide models are typically far more interpretable.Training a model to find patterns in a data set - typically an unlabeled data set. The most common use of unsupervised machine learning is to cluster data into groups of similar examples. For example - an unsupervised machine learning algorithm can cluster songs together based on various properties of the music. The resulting clusters can become an input to other machine learning algorithms (for example - to a music recommendation service). Clustering can be helpful in domains where true labels are hard to obtain. For example - in domains such as anti-abuse and fraud - clusters can help humans better understand the data. Another example of unsupervised machine learning is principal component analysis (PCA). For example - applying PCA on a data set containing the contents of millions of shopping carts might reveal that shopping carts containing lemons frequently also contain antacids. Compare with supervised machine learning.

19. Label

Training a model to find patterns in a data set - typically an unlabeled data set. The most common use of unsupervised machine learning is to cluster data into groups of similar examples. For example - an unsupervised machine learning algorithm can cluster songs together based on various properties of the music. The resulting clusters can become an input to other machine learning algorithms (for example - to a music recommendation service). Clustering can be helpful in domains where true labels are hard to obtain. For example - in domains such as anti-abuse and fraud - clusters can help humans better understand the data. Another example of unsupervised machine learning is principal component analysis (PCA). For example - applying PCA on a data set containing the contents of millions of shopping carts might reveal that shopping carts containing lemons frequently also contain antacids. Compare with supervised machine learning.In supervised learning - the "answer" or "result" portion of an example. Each example in a labeled data set consists of one or more features and a label. For instance - in a housing data set - the features might include the number of bedrooms - the number of bathrooms - and the age of the house - while the label might be the house's price. in a spam detection dataset - the features might include the subject line - the sender - and the email message itself - while the label would probably be either "spam" or "not spam."Synonym for loss.A feature that is not present among the input features - but is derived from one or more of them. Kinds of synthetic features include the following: Multiplying one feature by itself or by other feature(s). (These are termed feature crosses.) - Dividing one feature by a second feature. Bucketing a continuous feature into range bins. Features created by normalizing or scaling alone are not considered synthetic features.

20. Ensemble

A merger of the predictions of multiple models. You can create an ensemble via one or more of the following: different initializations - different hyperparameters - different overall structure - Deep and wide models are a kind of ensemble.A feature that is not present among the input features - but is derived from one or more of them. Kinds of synthetic features include the following: Multiplying one feature by itself or by other feature(s). (These are termed feature crosses.) - Dividing one feature by a second feature. Bucketing a continuous feature into range bins. Features created by normalizing or scaling alone are not considered synthetic features.Synonym for loss.Training a model to find patterns in a data set - typically an unlabeled data set. The most common use of unsupervised machine learning is to cluster data into groups of similar examples. For example - an unsupervised machine learning algorithm can cluster songs together based on various properties of the music. The resulting clusters can become an input to other machine learning algorithms (for example - to a music recommendation service). Clustering can be helpful in domains where true labels are hard to obtain. For example - in domains such as anti-abuse and fraud - clusters can help humans better understand the data. Another example of unsupervised machine learning is principal component analysis (PCA). For example - applying PCA on a data set containing the contents of millions of shopping carts might reveal that shopping carts containing lemons frequently also contain antacids. Compare with supervised machine learning.

21. Perplexity

One measure of how well a model is accomplishing its task. For example - suppose your task is to read the first few letters of a word a user is typing on a smartphone keyboard - and to offer a list of possible completion words. Perplexity - P - for this task is approximately the number of guesses you need to offer in order for your list to contain the actual word the user is trying to type. Perplexity is related to cross-entropy as follows:Synonym for loss.A feature that is not present among the input features - but is derived from one or more of them. Kinds of synthetic features include the following: Multiplying one feature by itself or by other feature(s). (These are termed feature crosses.) - Dividing one feature by a second feature. Bucketing a continuous feature into range bins. Features created by normalizing or scaling alone are not considered synthetic features.Training a model to find patterns in a data set - typically an unlabeled data set. The most common use of unsupervised machine learning is to cluster data into groups of similar examples. For example - an unsupervised machine learning algorithm can cluster songs together based on various properties of the music. The resulting clusters can become an input to other machine learning algorithms (for example - to a music recommendation service). Clustering can be helpful in domains where true labels are hard to obtain. For example - in domains such as anti-abuse and fraud - clusters can help humans better understand the data. Another example of unsupervised machine learning is principal component analysis (PCA). For example - applying PCA on a data set containing the contents of millions of shopping carts might reveal that shopping carts containing lemons frequently also contain antacids. Compare with supervised machine learning.

22. Regression Model

A type of model that outputs continuous (typically - floating-point) values. Compare with classification models - which output discrete values - such as "day lily" or "tiger lily."A feature that is not present among the input features - but is derived from one or more of them. Kinds of synthetic features include the following: Multiplying one feature by itself or by other feature(s). (These are termed feature crosses.) - Dividing one feature by a second feature. Bucketing a continuous feature into range bins. Features created by normalizing or scaling alone are not considered synthetic features.Training a model to find patterns in a data set - typically an unlabeled data set. The most common use of unsupervised machine learning is to cluster data into groups of similar examples. For example - an unsupervised machine learning algorithm can cluster songs together based on various properties of the music. The resulting clusters can become an input to other machine learning algorithms (for example - to a music recommendation service). Clustering can be helpful in domains where true labels are hard to obtain. For example - in domains such as anti-abuse and fraud - clusters can help humans better understand the data. Another example of unsupervised machine learning is principal component analysis (PCA). For example - applying PCA on a data set containing the contents of millions of shopping carts might reveal that shopping carts containing lemons frequently also contain antacids. Compare with supervised machine learning.Synonym for loss.

23. Overfitting

A feature that is not present among the input features - but is derived from one or more of them. Kinds of synthetic features include the following: Multiplying one feature by itself or by other feature(s). (These are termed feature crosses.) - Dividing one feature by a second feature. Bucketing a continuous feature into range bins. Features created by normalizing or scaling alone are not considered synthetic features.Training a model to find patterns in a data set - typically an unlabeled data set. The most common use of unsupervised machine learning is to cluster data into groups of similar examples. For example - an unsupervised machine learning algorithm can cluster songs together based on various properties of the music. The resulting clusters can become an input to other machine learning algorithms (for example - to a music recommendation service). Clustering can be helpful in domains where true labels are hard to obtain. For example - in domains such as anti-abuse and fraud - clusters can help humans better understand the data. Another example of unsupervised machine learning is principal component analysis (PCA). For example - applying PCA on a data set containing the contents of millions of shopping carts might reveal that shopping carts containing lemons frequently also contain antacids. Compare with supervised machine learning.Synonym for loss.Creating a model that matches the training data so closely that the model fails to make correct predictions on new data.

24. Scaling

Synonym for loss.Training a model to find patterns in a data set - typically an unlabeled data set. The most common use of unsupervised machine learning is to cluster data into groups of similar examples. For example - an unsupervised machine learning algorithm can cluster songs together based on various properties of the music. The resulting clusters can become an input to other machine learning algorithms (for example - to a music recommendation service). Clustering can be helpful in domains where true labels are hard to obtain. For example - in domains such as anti-abuse and fraud - clusters can help humans better understand the data. Another example of unsupervised machine learning is principal component analysis (PCA). For example - applying PCA on a data set containing the contents of millions of shopping carts might reveal that shopping carts containing lemons frequently also contain antacids. Compare with supervised machine learning.A commonly used practice in feature engineering to tame a feature's range of values to match the range of other features in the data set. For example - suppose that you want all floating-point features in the data set to have a range of 0 to 1. Given a particular feature's range of 0 to 500 - you could scale that feature by dividing each value by 500. See also normalization.A feature that is not present among the input features - but is derived from one or more of them. Kinds of synthetic features include the following: Multiplying one feature by itself or by other feature(s). (These are termed feature crosses.) - Dividing one feature by a second feature. Bucketing a continuous feature into range bins. Features created by normalizing or scaling alone are not considered synthetic features.

25. Instance

A feature that is not present among the input features - but is derived from one or more of them. Kinds of synthetic features include the following: Multiplying one feature by itself or by other feature(s). (These are termed feature crosses.) - Dividing one feature by a second feature. Bucketing a continuous feature into range bins. Features created by normalizing or scaling alone are not considered synthetic features.Training a model to find patterns in a data set - typically an unlabeled data set. The most common use of unsupervised machine learning is to cluster data into groups of similar examples. For example - an unsupervised machine learning algorithm can cluster songs together based on various properties of the music. The resulting clusters can become an input to other machine learning algorithms (for example - to a music recommendation service). Clustering can be helpful in domains where true labels are hard to obtain. For example - in domains such as anti-abuse and fraud - clusters can help humans better understand the data. Another example of unsupervised machine learning is principal component analysis (PCA). For example - applying PCA on a data set containing the contents of millions of shopping carts might reveal that shopping carts containing lemons frequently also contain antacids. Compare with supervised machine learning.Synonym for example.Synonym for loss.

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25 Questions

❤ If you liked Fatskills, consider supporting us by checking out The Life Manuals You Never Got.

About | Explore | User Guide | Topics | Subjects | Doubt Solver | Career Aptitude Test | Answers | Free Tools | OSHA Basics Quiz | What Should We Know? Privacy | Terms |

Without work one finishes nothing. - Ralph Waldo Emerson© 2026 Fatskills.com

All trademarks, logos and brand names are the property of their respective owners. All company, product and service names used in this website are for identification purposes only. Use of these names, trademarks and brands does not imply endorsement.

About | Explore | User Guide | Topics | Subjects | Doubt Solver | Career Aptitude Test | Answers | Free Tools | OSHA Basics Quiz | What Should We Know?
Privacy | Terms |

Without work one finishes nothing. - Ralph Waldo Emerson
© 2026 Fatskills.com