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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Applies transformers to columns of an array or pandas DataFrame. This estimator allows different columns or column subsets of the input to be transformed separately and the features generated by each…
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Encode categorical 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) features.…
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estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
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estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : RMSD
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estimation_procedure : 50 times Clustering - target_feature : decision
This database is also available through the UW CS ftp server: ftp ftp.cs.wisc.edu cd math-prog/cpo-dataset/machine-learn/WDBC/ Also can be found on UCI Machine Learning Repository:…
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A random forest classifier. A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive…
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Happy person.
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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…
26 runs0 likes0 downloads0 reach0 impact
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…
36 runs0 likes0 downloads0 reach0 impact
Jared Mccormick
Teacher dream discover important adult challenge. Trouble first would foreign position future serious.
West Ltd Joined 2021-08-19
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asdfasd
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761 instances - 42 features - classes - 630 missing values
Flores
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Syrian Arab Republic Joined 2021-08-18
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artificial with anomaly
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4032 instances - 3 features - classes - 0 missing values
Maria
Sparks, Cooper and Gutierrez Heard Island and McDonald Islands Joined 2021-08-16
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Michele Wolf
Stanton, Stevenson and Dominguez Central African Republic Joined 2021-08-14
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Nathaniel Petty
Wagner Ltd Joined 2021-08-14
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Gaussian Naive Bayes (GaussianNB) Can perform online updates to model parameters via :meth:`partial_fit`. For details on algorithm used to update feature means and variance online, see Stanford CS…
3 runs0 likes0 downloads0 reach0 impact
Logistic Regression (aka logit, MaxEnt) classifier. In the multiclass case, the training algorithm uses the one-vs-rest (OvR) scheme if the 'multi_class' option is set to 'ovr', and uses the…
5 runs0 likes0 downloads0 reach0 impact
Logistic Regression (aka logit, MaxEnt) classifier. In the multiclass case, the training algorithm uses the one-vs-rest (OvR) scheme if the 'multi_class' option is set to 'ovr', and uses the cross-…
10 runs0 likes0 downloads0 reach0 impact
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…
1 runs0 likes0 downloads0 reach0 impact
Naive Bayes classifier for multinomial models The multinomial Naive Bayes classifier is suitable for classification with discrete features (e.g., word counts for text classification). The multinomial…
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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…
2 runs0 likes0 downloads0 reach0 impact
Imputation transformer for completing missing values.
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C-Support Vector Classification. The implementation is based on libsvm. The fit time complexity is more than quadratic with the number of samples which makes it hard to scale to dataset with more than…
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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…
58 runs0 likes0 downloads0 reach50 impact
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…
6 runs0 likes0 downloads0 reach0 impact
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…
3 runs0 likes0 downloads0 reach0 impact
Multi-layer Perceptron classifier. This model optimizes the log-loss function using LBFGS or stochastic gradient descent. .. versionadded:: 0.18
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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…
5 runs0 likes0 downloads0 reach0 impact
Linear classifiers (SVM, logistic regression, a.o.) with SGD training. This estimator implements regularized linear models with stochastic gradient descent (SGD) learning: the gradient of the loss is…
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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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Naive Bayes classifier for multivariate Bernoulli models. Like MultinomialNB, this classifier is suitable for discrete data. The difference is that while MultinomialNB works with occurrence counts,…
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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…
6 runs0 likes0 downloads0 reach0 impact
Gradient Boosting for classification. GB builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage…
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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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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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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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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…
8 runs0 likes0 downloads0 reach0 impact
C-Support Vector Classification. The implementation is based on libsvm. The fit time complexity is more than quadratic with the number of samples which makes it hard to scale to dataset with more than…
0 runs0 likes0 downloads0 reach0 impact
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…
5 runs0 likes0 downloads0 reach0 impact
An extra-trees classifier. This class implements a meta estimator that fits a number of randomized decision trees (a.k.a. extra-trees) on various sub-samples of the dataset and uses averaging to…
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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…
8 runs0 likes0 downloads0 reach0 impact
Applies transformers to columns of an array or pandas DataFrame. EXPERIMENTAL: some behaviors may change between releases without deprecation. This estimator allows different columns or column subsets…
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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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Classifier implementing the k-nearest neighbors vote.
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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…
9 runs0 likes0 downloads0 reach0 impact
Applies transformers to columns of an array or pandas DataFrame. EXPERIMENTAL: some behaviors may change between releases without deprecation. This estimator allows different columns or column subsets…
0 runs0 likes0 downloads0 reach0 impact
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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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…
0 runs0 likes0 downloads0 reach0 impact
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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Feature selector that removes all low-variance features. This feature selection algorithm looks only at the features (X), not the desired outputs (y), and can thus be used for unsupervised learning.
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A random forest classifier. A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive…
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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…
2 runs0 likes0 downloads0 reach0 impact
Imputation transformer for completing missing values.
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Encode categorical integer features using a one-hot aka one-of-K scheme. The input to this transformer should be a matrix of integers, denoting the values taken on by categorical (discrete) features.…
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Logistic Regression (aka logit, MaxEnt) classifier. In the multiclass case, the training algorithm uses the one-vs-rest (OvR) scheme if the 'multi_class' option is set to 'ovr', and uses the cross-…
0 runs0 likes0 downloads0 reach0 impact
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…
1 runs0 likes0 downloads0 reach0 impact
Encode categorical integer features using a one-hot aka one-of-K scheme. The input to this transformer should be a matrix of integers, denoting the values taken on by categorical (discrete) features.…
0 runs0 likes0 downloads0 reach0 impact
Feature selector that removes all low-variance features. This feature selection algorithm looks only at the features (X), not the desired outputs (y), and can thus be used for unsupervised learning.
0 runs0 likes0 downloads0 reach0 impact
A random forest classifier. A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and use averaging to improve the predictive…
0 runs0 likes0 downloads0 reach0 impact
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…
2 runs0 likes0 downloads0 reach0 impact
Feature ranking with recursive feature elimination. Given an external estimator that assigns weights to features (e.g., the coefficients of a linear model), the goal of recursive feature elimination…
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An extra-trees classifier. This class implements a meta estimator that fits a number of randomized decision trees (a.k.a. extra-trees) on various sub-samples of the dataset and use averaging to…
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Linear Support Vector Classification. Similar to SVC with parameter kernel='linear', but implemented in terms of liblinear rather than libsvm, so it has more flexibility in the choice of penalties and…
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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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Scale features using statistics that are robust to outliers. This Scaler removes the median and scales the data according to the quantile range (defaults to IQR: Interquartile Range). The IQR is 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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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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Gradient Boosting for classification. GB builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage…
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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…
3 runs0 likes0 downloads0 reach0 impact
Approximate a kernel map using a subset of the training data. Constructs an approximate feature map for an arbitrary kernel using a subset of the data as basis.
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Gaussian Naive Bayes (GaussianNB) Can perform online updates to model parameters via `partial_fit` method. For details on algorithm used to update feature means and variance online, see Stanford CS…
0 runs0 likes0 downloads0 reach0 impact
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…
16 runs0 likes0 downloads0 reach0 impact
Imputation transformer for completing missing values.
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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…
2 runs0 likes0 downloads0 reach0 impact
Agglomerate features. Similar to AgglomerativeClustering, but recursively merges features instead of samples.
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Classifier implementing the k-nearest neighbors vote.
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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…
29 runs0 likes0 downloads0 reach0 impact
Imputation transformer for completing missing values.
0 runs0 likes0 downloads0 reach0 impact
A decision tree classifier.
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Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "predict", "predict_proba", "decision_function", "transform" and…
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A random forest classifier. A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and use averaging to improve the predictive…
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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…
1 runs0 likes0 downloads0 reach0 impact
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…
10 runs0 likes0 downloads0 reach0 impact
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…
12 runs0 likes0 downloads0 reach0 impact
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…
13 runs0 likes0 downloads0 reach0 impact
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…
7 runs0 likes0 downloads0 reach0 impact
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…
25 runs0 likes0 downloads0 reach0 impact
Encode categorical integer features using a one-hot aka one-of-K scheme. The input to this transformer should be a matrix of integers, denoting the values taken on by categorical (discrete) features.…
0 runs0 likes0 downloads0 reach0 impact
Feature selector that removes all low-variance features. This feature selection algorithm looks only at the features (X), not the desired outputs (y), and can thus be used for unsupervised learning.
0 runs0 likes0 downloads0 reach0 impact
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…
10 runs0 likes0 downloads0 reach0 impact
Multi-layer Perceptron classifier. This model optimizes the log-loss function using LBFGS or stochastic gradient descent. .. versionadded:: 0.18
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