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classify
classify
 
 
data
data
 
 
datasets
datasets
 
 
logistic-regression
logistic-regression
 
 
vectorizer
vectorizer
 
 
README.org
README.org
 
 
Scikit_Learn_Cheat_Sheet_Python.pdf
Scikit_Learn_Cheat_Sheet_Python.pdf
 
 
accuracy.py
accuracy.py
 
 
boston.csv
boston.csv
 
 
boston.py
boston.py
 
 
confusion-matrix.py
confusion-matrix.py
 
 
crossvalidation.py
crossvalidation.py
 
 
crossvalscore.py
crossvalscore.py
 
 
document_clustering.py
document_clustering.py
 
 
forest-classifier.py
forest-classifier.py
 
 
forest-regression.py
forest-regression.py
 
 
gram-matrix.py
gram-matrix.py
 
 
gridsearch.py
gridsearch.py
 
 
groupkfold.py
groupkfold.py
 
 
incremental-pca.py
incremental-pca.py
 
 
iris.data
iris.data
 
 
kernel-approximation.py
kernel-approximation.py
 
 
kfold-example.py
kfold-example.py
 
 
kfold.py
kfold.py
 
 
kmeans-text-clustering.py
kmeans-text-clustering.py
 
 
lab4.py
lab4.py
 
 
latentsemantic.py
latentsemantic.py
 
 
lda-simple.py
lda-simple.py
 
 
lda.py
lda.py
 
 
leaveoneout.py
leaveoneout.py
 
 
leavepout.py
leavepout.py
 
 
lfw.py
lfw.py
 
 
minimum-spaning-tree.py
minimum-spaning-tree.py
 
 
mlp.py
mlp.py
 
 
nooverfitting.py
nooverfitting.py
 
 
pca-choosing-components.py
pca-choosing-components.py
 
 
pca-example.py
pca-example.py
 
 
pca-interprenting.org
pca-interprenting.org
 
 
pca-nice.py
pca-nice.py
 
 
pca.py
pca.py
 
 
perceptron.py
perceptron.py
 
 
perceptron2.py
perceptron2.py
 
 
pipeline.py
pipeline.py
 
 
plot_adaboost_multiclass.py
plot_adaboost_multiclass.py
 
 
plot_calibration_curve.py
plot_calibration_curve.py
 
 
plot_classifier_comparison.py
plot_classifier_comparison.py
 
 
plot_cluster_comparison.py
plot_cluster_comparison.py
 
 
plot_cluster_iris.py
plot_cluster_iris.py
 
 
plot_color_quantization.py
plot_color_quantization.py
 
 
plot_compare_calibration.py
plot_compare_calibration.py
 
 
plot_compare_methods.py
plot_compare_methods.py
 
 
plot_compare_reduction.py
plot_compare_reduction.py
 
 
plot_confusion_matrix.py
plot_confusion_matrix.py
 
 
plot_covariance_estimation.py
plot_covariance_estimation.py
 
 
plot_denoise.py
plot_denoise.py
 
 
plot_digits_agglomeration.py
plot_digits_agglomeration.py
 
 
plot_digits_classification.py
plot_digits_classification.py
 
 
plot_edge_modes.py
plot_edge_modes.py
 
 
plot_ensemble_oob.py
plot_ensemble_oob.py
 
 
plot_face_recognition.py
plot_face_recognition.py
 
 
plot_face_segmentation.py
plot_face_segmentation.py
 
 
plot_forest_importances.py
plot_forest_importances.py
 
 
plot_grid_search_refit_callable.py
plot_grid_search_refit_callable.py
 
 
plot_iris1.py
plot_iris1.py
 
 
plot_kernel_ridge_regression.py
plot_kernel_ridge_regression.py
 
 
plot_kmeans_assumptions.py
plot_kmeans_assumptions.py
 
 
plot_lasso_and_elasticnet.py
plot_lasso_and_elasticnet.py
 
 
plot_learning_curve.py
plot_learning_curve.py
 
 
plot_lle_digits.py
plot_lle_digits.py
 
 
plot_marching_cubes.py
plot_marching_cubes.py
 
 
plot_mean_shift.py
plot_mean_shift.py
 
 
plot_model_complexity_influence.py
plot_model_complexity_influence.py
 
 
plot_multi_task_lasso_support.py
plot_multi_task_lasso_support.py
 
 
plot_nested_cross_validation_iris.py
plot_nested_cross_validation_iris.py
 
 
plot_omp.py
plot_omp.py
 
 
plot_outlier_detection_housing.py
plot_outlier_detection_housing.py
 
 
plot_pca_iris.py
plot_pca_iris.py
 
 
plot_precision_recall.py
plot_precision_recall.py
 
 
plot_random_forest_embedding.py
plot_random_forest_embedding.py
 
 
plot_roc.py
plot_roc.py
 
 
plot_roc_curve_visualization_api.py
plot_roc_curve_visualization_api.py
 
 
plot_roc_simple.py
plot_roc_simple.py
 
 
plot_sgd_iris.py
plot_sgd_iris.py
 
 
plot_sgd_weighted_samples.py
plot_sgd_weighted_samples.py
 
 
plot_spectral_grid.py
plot_spectral_grid.py
 
 
plot_tree_regression.py
plot_tree_regression.py
 
 
plot_unveil_tree_structure.py
plot_unveil_tree_structure.py
 
 
plot_voting_regressor.py
plot_voting_regressor.py
 
 
pulsar_stars.csv
pulsar_stars.csv
 
 
qda.py
qda.py
 
 
repeatedkfold.py
repeatedkfold.py
 
 
ridge.py
ridge.py
 
 
roc-auc-score.py
roc-auc-score.py
 
 
roc-simple.py
roc-simple.py
 
 
roc.py
roc.py
 
 
scikit-example.py
scikit-example.py
 
 
scikit-knn.py
scikit-knn.py
 
 
scikit-machine-learning-algorithms.png
scikit-machine-learning-algorithms.png
 
 
scikit-model.py
scikit-model.py
 
 
scikit-multiclass.py
scikit-multiclass.py
 
 

README.org

Tutorial / Howto

http://scikit-learn.org/stable/tutorial/index.html

Naive Bayes Classification

  • To mark an email as spam or not spam
  • Classify a news article about technology, politics, or sports
  • Check a piece of text expressing positive emotions, or negative emotions?
  • Used for face recognition software.

Logistic Regression

In general, regressions can be used in real-world applications such as:

  • Credit Scoring
  • Measuring the success rates of marketing campaigns
  • Predicting the revenues of a certain product
  • Is there going to be an earthquake on a particular day?

Support Vector Machine

  • Display advertising
  • Human splice site recognition
  • Image-based gender detection
  • Large-scale image classification

Clustering Algorithms

Clustering is the task of grouping a set of objects such that objects in the same group (cluster) are more similar to each other than to those in other groups.

Principal Component Analysis

  • Compression
  • Simplifying data for easier learning
  • Visualization

Singular Value Descomposition

In computer vision, the 1st face recognition algorithms used PCA and SVD in order to represent faces as a linear combination of “eigenfaces”, do dimensionality reduction, and then match faces to identities via simple methods; although modern methods are much more sophisticated, many still depend on similar techniques.

Independent Component Analysis

  • Digital images
  • Document databases
  • Economic indicators
  • Psychometric measurements