Usage Sample
''''''''''''
.. code:: python
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearntools import train_evaluate, search_model_params
if __name__ == '__main__':
X, y = np.arange(20).reshape((10, 2)), range(10)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
model = RandomForestClassifier(n_estimators=837, bootstrap=False)
train_evaluate(model, X_train, X_test, y_train, y_test)
param_grid = {'n_estimators': np.arange(800, 820, 1), 'bootstrap': [False, True]}
search_model_params(RandomForestClassifier, X_train, X_test, y_train, y_test, param_grid, result_num=3)
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"description": "Usage Sample\n''''''''''''\n\n.. code:: python\n\n import numpy as np\n from sklearn.ensemble import RandomForestClassifier\n from sklearn.model_selection import train_test_split\n from sklearntools import train_evaluate, search_model_params\n\n if __name__ == '__main__':\n X, y = np.arange(20).reshape((10, 2)), range(10)\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n\n model = RandomForestClassifier(n_estimators=837, bootstrap=False)\n train_evaluate(model, X_train, X_test, y_train, y_test)\n\n param_grid = {'n_estimators': np.arange(800, 820, 1), 'bootstrap': [False, True]}\n\t search_model_params(RandomForestClassifier, X_train, X_test, y_train, y_test, param_grid, result_num=3)\n\n\n",
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