Flow
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Implementation of the scikit-learn API for XGBoost classification.
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Probability calibration with isotonic regression or logistic regression. The calibration is based on the :term:`decision_function` method of the `base_estimator` if it exists, else on…
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Imputation transformer for completing missing values.
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Probability calibration with isotonic regression or logistic regression. The calibration is based on the :term:`decision_function` method of the `base_estimator` if it exists, else on…
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Standardize features by removing the mean and scaling to unit variance The standard score of a sample `x` is calculated as: z = (x - u) / s where `u` is the mean of the training samples or zero if…
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C-Support Vector Classification. The implementation is based on libsvm. The fit time scales at least quadratically with the number of samples and may be impractical beyond tens of thousands of…
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Probability calibration with isotonic regression or sigmoid. See glossary entry for :term:`cross-validation estimator`. With this class, the base_estimator is fit on the train set of the…
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Standardize features by removing the mean and scaling to unit variance The standard score of a sample `x` is calculated as: z = (x - u) / s where `u` is the mean of the training samples or zero if…
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Linear least squares with l2 regularization. Minimizes the objective function:: ||y - Xw||^2_2 + alpha * ||w||^2_2 This model solves a regression model where the loss function is the linear least…
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Regression based on k-nearest neighbors. The target is predicted by local interpolation of the targets associated of the nearest neighbors in the training set.
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Ordinary least squares Linear Regression. LinearRegression fits a linear model with coefficients w = (w1, ..., wp) to minimize the residual sum of squares between the observed targets in the dataset,…
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
0 runs0 likes0 downloads0 reach0 impact
Automatically created pytorch flow.
0 runs0 likes0 downloads0 reach0 impact
Automatically created pytorch flow.
0 runs0 likes0 downloads0 reach0 impact
Automatically created pytorch flow.
0 runs0 likes0 downloads0 reach0 impact
Automatically created pytorch flow.
0 runs0 likes0 downloads0 reach0 impact
Automatically created pytorch flow.
0 runs0 likes0 downloads0 reach0 impact
Automatically created pytorch flow.
0 runs0 likes0 downloads0 reach0 impact
Automatically created pytorch flow.
0 runs0 likes0 downloads0 reach0 impact
Automatically created pytorch flow.
0 runs0 likes0 downloads0 reach0 impact
Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
0 runs0 likes0 downloads0 reach0 impact
C-Support Vector Classification. The implementation is based on libsvm. The fit time scales at least quadratically with the number of samples and may be impractical beyond tens of thousands of…
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Imputation transformer for completing missing values.
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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A decision tree classifier.
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Dimensionality Reduction Model
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Principal component analysis (PCA) Linear dimensionality reduction using Singular Value Decomposition of the data to project it to a lower dimensional space. It uses the LAPACK implementation of the…
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Pre-process Block
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Classifier implementing the k-nearest neighbors vote.
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K-Means clustering
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Ensemble Model
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Explainer Model
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Select features according to a percentile of the highest scores.
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Agglomerate features. Similar to AgglomerativeClustering, but recursively merges features instead of samples.
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A decision tree classifier.
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Standardize features by removing the mean and scaling to unit variance Centering and scaling happen independently on each feature by computing the relevant statistics on the samples in the training…
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Imputation transformer for completing missing values.
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Encode categorical integer features as a one-hot numeric array. The input to this transformer should be an array-like of integers or strings, denoting the values taken on by categorical (discrete)…
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An AdaBoost classifier. An AdaBoost [1] classifier is a meta-estimator that begins by fitting a classifier on the original dataset and then fits additional copies of the classifier on the same dataset…
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A decision tree classifier.
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Binary indicators for missing values.
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Imputation transformer for completing missing values.
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
0 runs0 likes0 downloads0 reach0 impact
Imputation transformer for completing missing values.
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A decision tree classifier.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
0 runs0 likes0 downloads0 reach0 impact
Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
0 runs0 likes0 downloads0 reach0 impact
Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
0 runs0 likes0 downloads0 reach0 impact
Automatically created pytorch flow.
0 runs0 likes0 downloads0 reach0 impact
Automatically created pytorch flow.
0 runs0 likes0 downloads0 reach0 impact
Automatically created pytorch flow.
0 runs0 likes0 downloads0 reach0 impact
Automatically created pytorch flow.
0 runs0 likes0 downloads0 reach0 impact
Automatically created pytorch flow.
0 runs0 likes0 downloads0 reach0 impact
Automatically created pytorch flow.
0 runs0 likes0 downloads0 reach0 impact
Automatically created pytorch flow.
0 runs0 likes0 downloads0 reach0 impact
Automatically created pytorch flow.
0 runs0 likes0 downloads0 reach0 impact
Automatically created pytorch flow.
0 runs0 likes0 downloads0 reach0 impact
Automatically created pytorch flow.
0 runs0 likes0 downloads0 reach0 impact
Automatically created pytorch flow.
0 runs0 likes0 downloads0 reach0 impact
Automatically created pytorch flow.
0 runs0 likes0 downloads0 reach0 impact
Automatically created pytorch flow.
0 runs0 likes0 downloads0 reach0 impact
Automatically created pytorch flow.
0 runs0 likes0 downloads0 reach0 impact
Automatically created pytorch flow.
0 runs0 likes0 downloads0 reach0 impact
Automatically created pytorch flow.
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