本文整理汇总了Python中sklearn.tree.DecisionTreeClassifier.get_params方法的典型用法代码示例。如果您正苦于以下问题：Python DecisionTreeClassifier.get_params方法的具体用法？Python DecisionTreeClassifier.get_params怎么用？Python DecisionTreeClassifier.get_params使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.tree.DecisionTreeClassifier的用法示例。
在下文中一共展示了DecisionTreeClassifier.get_params方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: dtree
# 需要导入模块: from sklearn.tree import DecisionTreeClassifier [as 别名]
# 或者: from sklearn.tree.DecisionTreeClassifier import get_params [as 别名]
def dtree():
##Decision Tree
from sklearn.tree import DecisionTreeClassifier
dtree = DecisionTreeClassifier(criterion="entropy",max_depth=20, min_samples_leaf=4, splitter = "best", min_samples_split=10)
dtree.fit(Xtrn,Ytrn)
print dtree.get_params()
print dtree.score(Xtrn,Ytrn)
print dtree.score(Xval1, Yval1)
print dtree.score(Xval2, Yval2)
print dtree.score(Xval3, Yval3)示例2: tree
# 需要导入模块: from sklearn.tree import DecisionTreeClassifier [as 别名]
# 或者: from sklearn.tree.DecisionTreeClassifier import get_params [as 别名]
def tree(labels,X,df,i):
tree = DT(max_depth = 4)
tree.fit(X,labels)
impt = tree.feature_importances_
para = tree.get_params()
export_graphviz(tree, out_file = OUTPUT_DIRECTORY+str(i)+"_tree.dot", feature_names = df.columns)
return impt示例3: NuSVR
# 需要导入模块: from sklearn.tree import DecisionTreeClassifier [as 别名]
# 或者: from sklearn.tree.DecisionTreeClassifier import get_params [as 别名]
print 'NuSVC config:'
print nusvc.get_params()
nusvc.fit(smr_train.feature_matrix, smr_train.labels)
nusvc_score_train = nusvc.score(smr_train.feature_matrix, smr_train.labels)
print 'NuSVC precision train: {}'.format(nusvc_score_train)
nusvc_score_test = nusvc.score(smr_test.feature_matrix, smr_test.labels)
print 'NuSVC precision test: {}'.format(nusvc_score_test)
print ''
nusvr = NuSVR()
print 'NuSVR config:'
print nusvr.get_params()
nusvr.fit(smr_train.feature_matrix, smr_train.labels)
nusvr_score_train = svc.score(smr_train.feature_matrix, smr_train.labels)
print 'NuSVR precision train: {}'.format(nusvr_score_train)
nusvr_score_test = nusvr.score(smr_test.feature_matrix, smr_test.labels)
print 'NuSVR precision test: {}'.format(nusvr_score_test)
print ''
dtc = DecisionTreeClassifier()
print 'DecisionTreeClassifier config:'
print dtc.get_params()
dtc.fit(smr_train.feature_matrix, smr_train.labels)
dtc_score_train = dtc.score(smr_train.feature_matrix, smr_train.labels)
print 'DecisionTreeClassifier precision train: {}'.format(dtc_score_train)
dtc_score_test = dtc.score(smr_test.feature_matrix, smr_test.labels)
print 'DecisionTreeClassifier precision test: {}'.format(dtc_score_test)
print classification_report(smr_test.labels, dtc.predict(smr_test.feature_matrix))
print ''