Scikit-learn is a focal point for data science work with Python, so it pays to know which methods you need most. The following table provides a brief overview of the most important methods used for data analysis.
| 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 |
