sklearn_evaluation.grid
#
RandomForestClassifierGrid#
- class sklearn_evaluation.grid.RandomForestClassifierGrid(grid, cv=3, verbose=0)#
- confusion_matrix()#
Plots a confusion matrix based on GridSearchCV.best_estimator_.
- Returns
ax – Axes containing the plot
- Return type
matplotlib Axes
Examples
from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from sklearn_evaluation import grid # generate data X, y = make_classification( n_samples=100, n_features=2, n_informative=2, n_redundant=0, random_state=0 ) # split data into train and test X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, random_state=0 ) model = grid.RandomForestClassifierGrid(grid="tiny") model.fit(X_train, y_train) model.set_test_data(X_test, y_test) model.confusion_matrix()
- feature_importances()#
Plots feature importances based on GridSearchCV.best_estimator_.
- Returns
ax – Axes containing the plot
- Return type
matplotlib Axes
Examples
from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from sklearn_evaluation import grid # generate data X, y = make_classification( n_samples=100, n_features=2, n_informative=2, n_redundant=0, random_state=0 ) # split data into train and test X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, random_state=0 ) model = grid.RandomForestClassifierGrid(grid="tiny") model.fit(X_train, y_train) model.set_test_data(X_test, y_test) model.feature_importances()
- fit(X, y)#
Fit estimator.
- Parameters
X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The input samples. Use
dtype=np.float32
for maximum efficiency. Sparse matrices are also supported, use sparsecsc_matrix
for maximum efficiency.y (Ignored) – Not used, present for API consistency by convention.
- Returns
self – Returns the instance itself.
- Return type
object
- grid_search_results(change='n_estimators', kind='line')#
Plots grid search results based on GridSearchCV.best_estimator_.
- Parameters
change (str or iterable with len<=2) – Parameter to change
kind (['line', 'bar']) – This only applies whe change is a single parameter. Changes the type of plot
- Returns
ax – Axes containing the plot
- Return type
matplotlib Axes
- roc()#
Plots an ROC based on GridSearchCV.best_estimator_.
- Returns
ax – Axes containing the plot
- Return type
matplotlib Axes
Examples
from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from sklearn_evaluation import grid # generate data X, y = make_classification( n_samples=100, n_features=2, n_informative=2, n_redundant=0, random_state=0 ) # split data into train and test X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, random_state=0 ) model = grid.RandomForestClassifierGrid(grid="tiny") model.fit(X_train, y_train) model.set_test_data(X_test, y_test) model.roc()
- set_test_data(X_test, y_test) None #
Set the test data
- Parameters
X_test (array-like of shape (n_samples, n_features)) – Training data, where n_samples is the number of samples and n_features is the number of features.
y_test (array-like of shape (n_samples,)) – The target variable for supervised learning problems.