PYTHON

decision tree best param

from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import GridSearchCV

def dtree_grid_search(X,y,nfolds):
    #create a dictionary of all values we want to test
    param_grid = { 'criterion':['gini','entropy'],'max_depth': np.arange(3, 15)}
    # decision tree model
    dtree_model=DecisionTreeClassifier()
    #use gridsearch to test all values
    dtree_gscv = GridSearchCV(dtree_model, param_grid, cv=nfolds)
    #fit model to data
    dtree_gscv.fit(X, y)
    return dtree_gscv.best_params_

decision tree best param

from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import GridSearchCV

def dtree_grid_search(X,y,nfolds):
    #create a dictionary of all values we want to test
    param_grid = { 'criterion':['gini','entropy'],'max_depth': np.arange(3, 15)}
    # decision tree model
    dtree_model=DecisionTreeClassifier()
    #use gridsearch to test all values
    dtree_gscv = GridSearchCV(dtree_model, param_grid, cv=nfolds)
    #fit model to data
    dtree_gscv.fit(X, y)
    return dtree_gscv.best_params_

decision tree best param

from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import GridSearchCV

def dtree_grid_search(X,y,nfolds):
    #create a dictionary of all values we want to test
    param_grid = { 'criterion':['gini','entropy'],'max_depth': np.arange(3, 15)}
    # decision tree model
    dtree_model=DecisionTreeClassifier()
    #use gridsearch to test all values
    dtree_gscv = GridSearchCV(dtree_model, param_grid, cv=nfolds)
    #fit model to data
    dtree_gscv.fit(X, y)
    return dtree_gscv.best_params_

decision tree best param

from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import GridSearchCV

def dtree_grid_search(X,y,nfolds):
    #create a dictionary of all values we want to test
    param_grid = { 'criterion':['gini','entropy'],'max_depth': np.arange(3, 15)}
    # decision tree model
    dtree_model=DecisionTreeClassifier()
    #use gridsearch to test all values
    dtree_gscv = GridSearchCV(dtree_model, param_grid, cv=nfolds)
    #fit model to data
    dtree_gscv.fit(X, y)
    return dtree_gscv.best_params_

decision tree best param

from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import GridSearchCV

def dtree_grid_search(X,y,nfolds):
    #create a dictionary of all values we want to test
    param_grid = { 'criterion':['gini','entropy'],'max_depth': np.arange(3, 15)}
    # decision tree model
    dtree_model=DecisionTreeClassifier()
    #use gridsearch to test all values
    dtree_gscv = GridSearchCV(dtree_model, param_grid, cv=nfolds)
    #fit model to data
    dtree_gscv.fit(X, y)
    return dtree_gscv.best_params_

decision tree best param

from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import GridSearchCV

def dtree_grid_search(X,y,nfolds):
    #create a dictionary of all values we want to test
    param_grid = { 'criterion':['gini','entropy'],'max_depth': np.arange(3, 15)}
    # decision tree model
    dtree_model=DecisionTreeClassifier()
    #use gridsearch to test all values
    dtree_gscv = GridSearchCV(dtree_model, param_grid, cv=nfolds)
    #fit model to data
    dtree_gscv.fit(X, y)
    return dtree_gscv.best_params_