# Classes¶

## Summary¶

class |
class parent |
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Base class to build a regressor on timeseries. The class computes one or several predictions at each time, between … |
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Converts sklearn.decomposition.NMF into a predictor so that the prediction does not involve training even … |
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Stores information when the outputs of a pipeline is computed. It as added by function @see fct alter_pipeline_for_debugging. … |
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Base for all timeseries preprocessing automatically applied within a predictor. |
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Base for transform which transforms the features and the targets at the same time. It must also return another transform … |
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Base class to build a predictor on timeseries. The class computes one or several predictions at each time, between … |
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Does something similar to what DictVectorizer … |
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Applies a |
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Common class to implement various version of mean square error. … |
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Defines a constraint k-means. Clusters are modified to have an equal size. The algorithm is initialized … |
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Customized MLP Perceptron based on BaseMultilayerPerceptron. … |
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Fits a logistic regression, then fits two other logistic regression for every observation on both sides of the border. … |
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Dummy regressor for time series. Use past values as prediction. |
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Generates extended features such as polynomial features. |
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Unable to process a type. |
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The transform is used to apply a function on a the target, predict, then transform the target back before scoring. … |
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Trains multiple regressors to provide a confidence interval on prediction. It only works for single regression. … |
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K-Means clustering with either norm L1 or L2. See notebook KMeans with norm L1 for an example. |
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Criterion which computes the mean square error assuming points falling into one node are approximated by a line … |
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Implements a cache to reduce the number of trainings a grid search has to do. |
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Overloads method _word_ngrams … |
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The transform is used to permute targets, predict, then permute the target back before scoring. nan values remain … |
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Uses a decision tree to split the space of features into buckets and trains a logistic regression (default) … |
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Uses a decision tree to split the space of features into buckets and trains a linear regression on each of them. … |
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Uses a decision tree to split the space of features into buckets and trains a linear regression (default) on … |
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Implements a kind of piecewise linear regression by modifying the criterion used by the algorithm which builds a decision … |
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Same as sklearn.pipeline.Pipeline but it can skip training if it detects a step was already trained the … |
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t-SNE is an interesting transform which can only be used to study data as there is no way to reproduce the … |
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Quantile Linear Regression or linear regression trained with norm L1. This class inherits from sklearn.linear_models.LinearRegression. … |
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Quantile MLP Regression or neural networks regression trained with norm L1. This class inherits from sklearn.neural_networks.MLPRegressor. … |
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Extends class |
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Extends class |
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Implements a kind of local search engine which looks for similar results assuming they are vectors. The class is … |
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Implements mean square error criterion in a non efficient … |
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Criterion which computes the mean square error assuming points falling into one node are approximated by a constant. … |
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Pattern of a |
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Defines a custom classifier. |
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Pattern of a |
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Defines a custom regressor. |
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Pattern of a |
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A |
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Un |
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custom exception |
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Defines a class to store parameters of a |
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Computes timeseries differences. |
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Computes the reverse of |
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Addition to sklearn.base.RegressorMixin. |
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Inherits from |
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Inherits from |
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Wraps a predictor or a transformer in a transformer. This model is frozen: it cannot be trained and only computes … |
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Meta-estimator to classify on a transformed target. Useful for applying permutation transformation in classification … |
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Meta-estimator to regress on a transformed target. Useful for applying a non-linear transformation in regression … |
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Describes the tree structure hold by class |