| Syntax |
Usage |
Description |
model_selection.cross_val_score |
Cross-validation phase |
Estimate the cross-validation score |
model_selection.KFold |
Cross-validation phase |
Divide the dataset into k folds for cross validation |
model_selection.StratifiedKFold |
Cross-validation phase |
Stratified validation that takes into account the distribution of the classes you predict |
model_selection.train_test_split |
Cross-validation phase |
Split your data into training and test sets |
decomposition.PCA |
Dimensionality reduction |
Principal component analysis (PCA) |
decomposition.RandomizedPCA |
Dimensionality reduction |
Principal component analysis (PCA) using randomized SVD |
feature_extraction.FeatureHasher |
Preparing your data |
The hashing trick, allowing you to accommodate a large number of features in your dataset |
feature_extraction.text.CountVectorizer |
Preparing your data |
Convert text documents into a matrix of count data |
feature_extraction.text.HashingVectorizer |
Preparing your data |
Directly convert your text using the hashing trick |
feature_extraction.text.TfidfVectorizer |
Preparing your data |
Creates a dataset of TF-IDF features |
feature_selection.RFECV |
Feature selection |
Automatic feature selection |
model_selection.GridSearchCV |
Optimization |
Exhaustive search in order to maximize a machine learning algorithm |
linear_model.LinearRegression |
Prediction |
Linear regression |
linear_model.LogisticRegression |
Prediction |
Linear logistic regression |
metrics.accuracy_score |
Solution evaluation |
Accuracy classification score |
metrics.f1_score |
Solution evaluation |
Compute the F1 score, balancing accuracy and recall |
metrics.mean_absolute_error |
Solution evaluation |
Mean absolute error regression error |
metrics.mean_squared_error |
Solution evaluation |
Mean squared error regression error |
metrics.roc_auc_score |
Solution evaluation |
Compute Area Under the Curve (AUC) from prediction scores |
naive_bayes.MultinomialNB |
Prediction |
Multinomial Naïve Bayes |
neighbors.KNeighborsClassifier |
Prediction |
K-Neighbors classification |
preprocessing.Binarizer |
Preparing your data |
Create binary variables (feature values to 0 or 1) |
preprocessing.Imputer |
Preparing your data |
Missing values imputation |
preprocessing.MinMaxScaler |
Preparing your data |
Create variables bound by a minimum and maximum value |
preprocessing.OneHotEncoder |
Preparing your data |
Transform categorical integer features into binary ones |
preprocessing.StandardScaler |
Preparing your data |
Variable standardization by removing the mean and scaling to unit variance |
