mlxtend
Home
User Guide
User Guide Index
classifier
Adaline: Adaptive Linear Neuron Classifier
EnsembleVoteClassifier: A majority voting classifier
LogisticRegression: A binary classifier
MultilayerPerceptron: A simple multilayer neural network
OneRClassifier: One Rule (OneR) method for classfication
Perceptron: A simple binary classifier
SoftmaxRegression: Multiclass version of logistic regression
StackingClassifier: Simple stacking
StackingCVClassifier: Stacking with cross-validation
cluster
Kmeans: k-means clustering
data
autompg_data: The Auto-MPG dataset for regression
boston_housing_data: The Boston housing dataset for regression
iris_data: The 3-class iris dataset for classification
loadlocal_mnist: A function for loading MNIST from the original ubyte files
make_multiplexer_dataset: A function for creating multiplexer data
mnist_data: A subset of the MNIST dataset for classification
three_blobs_data: The synthetic blobs for classification
wine_data: A 3-class wine dataset for classification
evaluate
accuracy_score: Computing standard, balanced, and per-class accuracy
bias_variance_decomp: Bias-variance decomposition for classification and regression losses
bootstrap: The ordinary nonparametric boostrap for arbitrary parameters
bootstrap_point632_score: The .632 and .632+ boostrap for classifier evaluation
BootstrapOutOfBag: A scikit-learn compatible version of the out-of-bag bootstrap
cochrans_q: Cochran's Q test for comparing multiple classifiers
combined_ftest_5x2cv: 5x2cv combined F test for classifier comparisons
confusion_matrix: creating a confusion matrix for model evaluation
create_counterfactual: Interpreting models via counterfactuals
feature_importance_permutation: Estimate feature importance via feature permutation.
ftest: F-test for classifier comparisons
GroupTimeSeriesSplit: A scikit-learn compatible version of the time series validation with groups
lift_score: Lift score for classification and association rule mining
mcnemar_table: Contingency table for McNemar's test
mcnemar_tables: contingency tables for McNemar's test and Cochran's Q test
mcnemar: McNemar's test for classifier comparisons
paired_ttest_5x2cv: 5x2cv paired t test for classifier comparisons
paired_ttest_kfold_cv: K-fold cross-validated paired t test
paired_ttest_resample: Resampled paired t test
permutation_test: Permutation test for hypothesis testing
PredefinedHoldoutSplit: Utility for the holdout method compatible with scikit-learn
RandomHoldoutSplit: split a dataset into a train and validation subset for validation
scoring: computing various performance metrics
feature_extraction
LinearDiscriminantAnalysis: Linear discriminant analysis for dimensionality reduction
PrincipalComponentAnalysis: Principal component analysis (PCA) for dimensionality reduction
RBFKernelPCA
feature_selection
ColumnSelector: Scikit-learn utility function to select specific columns in a pipeline
ExhaustiveFeatureSelector: Optimal feature sets by considering all possible feature combinations
SequentialFeatureSelector: The popular forward and backward feature selection approaches (including floating variants)
file_io
find_filegroups: Find files that only differ via their file extensions
find_files: Find files based on substring matches
frequent_patterns
Apriori
Association rules
Fpgrowth
Fpmax
math
num_combinations: combinations for creating subsequences of k elements
num_permutations: number of permutations for creating subsequences of k elements
vectorspace_dimensionality: compute the number of dimensions that a set of vectors spans
vectorspace_orthonormalization: Converts a set of linearly independent vectors to a set of orthonormal basis vectors
plotting
Scategory_scatter: Create a scatterplot with categories in different colors
checkerboard_plot: Create a checkerboard plot in matplotlib
plot_pca_correlation_graph: plot correlations between original features and principal components
ecdf: Create an empirical cumulative distribution function plot
enrichment_plot: create an enrichment plot for cumulative counts
heatmap: Create a heatmap in matplotlib
plot_confusion_matrix: Visualize confusion matrices
plot_decision_regions: Visualize the decision regions of a classifier
plot_learning_curves: Plot learning curves from training and test sets
plot_linear_regression: A quick way for plotting linear regression fits
plot_sequential_feature_selection: Visualize selected feature subset performances from the SequentialFeatureSelector
scatterplotmatrix: visualize datasets via a scatter plot matrix
scatter_hist: create a scatter histogram plot
stacked_barplot: Plot stacked bar plots in matplotlib
preprocessing
CopyTransformer: A function that creates a copy of the input array in a scikit-learn pipeline
DenseTransformer: Transforms a sparse into a dense NumPy array, e.g., in a scikit-learn pipeline
MeanCenterer: column-based mean centering on a NumPy array
MinMaxScaling: Min-max scaling fpr pandas DataFrames and NumPy arrays
One hot encoding
shuffle_arrays_unison: shuffle arrays in a consistent fashion
standardize: A function to standardize columns in a 2D NumPy array
TransactionEncoder
regressor
LinearRegression: An implementation of ordinary least-squares linear regression
StackingCVRegressor: stacking with cross-validation for regression
StackingRegressor: a simple stacking implementation for regression
text
generalize_names: convert names into a generalized format
generalize_names_duplcheck: Generalize names while preventing duplicates among different names
tokenizer_emoticons: tokenizers for emoticons
utils
Counter: A simple progress counter
API
Mlxtend.classifier
Mlxtend.cluster
Mlxtend.data
Mlxtend.evaluate
Mlxtend.feature extraction
Mlxtend.feature selection
Mlxtend.file io
Mlxtend.frequent patterns
Mlxtend.plotting
Mlxtend.preprocessing
Mlxtend.regressor
Mlxtend.text
Mlxtend.utils
Installation
About
Release Notes
Code of Conduct
How To Contribute
Contributors
License
Citing Mlxtend
Discuss
Search
GitHub
404
Page not found
Search
×
Close
From here you can search these documents. Enter your search terms below.
Keyboard Shortcuts
×
Close
Keys
Action
?
Open this help
n
Next page
p
Previous page
s
Search