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Python RandomForestClassifier.score方法代码示例

本文整理汇总了Python中sklearn.ensemble.RandomForestClassifier.score方法的典型用法代码示例。如果您正苦于以下问题:Python RandomForestClassifier.score方法的具体用法?Python RandomForestClassifier.score怎么用?Python RandomForestClassifier.score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sklearn.ensemble.RandomForestClassifier的用法示例。


在下文中一共展示了RandomForestClassifier.score方法的15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: RF

# 需要导入模块: from sklearn.ensemble import RandomForestClassifier [as 别名]
# 或者: from sklearn.ensemble.RandomForestClassifier import score [as 别名]
class RF(object):

	def __init__(self, train, test, trees):
		self.trees = trees
		self.train = train
		self.test = test
		self.features, self.label = self.find_features()
		self.forest = self.train_forest()

	def find_features(self):
		features = self.train.columns.values.tolist()
		label = features.pop(0)

		return features, label

	def train_forest(self):
		self.forest = RandomForestClassifier( n_estimators = self.trees, bootstrap = False, max_features = 'sqrt')
		self.forest.fit( self.train[self.features], self.train[self.label] )
		return self.forest

	def score_forest(self):
		print self.forest.score( self.test[self.features], self.test[self.label] )

	def predict_test(self):
		return self.forest.predict( self.test[self.features] )
开发者ID:benjamin-limoges,项目名称:Machine_Learning,代码行数:27,代码来源:RF.py

示例2: test_iris

# 需要导入模块: from sklearn.ensemble import RandomForestClassifier [as 别名]
# 或者: from sklearn.ensemble.RandomForestClassifier import score [as 别名]
def test_iris():
    """Check consistency on dataset iris."""
    for c in ("gini", "entropy"):
        # Random forest
        clf = RandomForestClassifier(n_estimators=10, criterion=c,
                                     random_state=1)
        clf.fit(iris.data, iris.target)
        score = clf.score(iris.data, iris.target)
        assert score > 0.9, "Failed with criterion %s and score = %f" % (c,
                                                                         score)

        clf = RandomForestClassifier(n_estimators=10, criterion=c,
                                     max_features=2, random_state=1)
        clf.fit(iris.data, iris.target)
        score = clf.score(iris.data, iris.target)
        assert score > 0.5, "Failed with criterion %s and score = %f" % (c,
                                                                         score)

        # Extra-trees
        clf = ExtraTreesClassifier(n_estimators=10, criterion=c,
                                   random_state=1)
        clf.fit(iris.data, iris.target)
        score = clf.score(iris.data, iris.target)
        assert score > 0.9, "Failed with criterion %s and score = %f" % (c,
                                                                         score)

        clf = ExtraTreesClassifier(n_estimators=10, criterion=c,
                                   max_features=2, random_state=1)
        clf.fit(iris.data, iris.target)
        score = clf.score(iris.data, iris.target)
        assert score > 0.9, "Failed with criterion %s and score = %f" % (c,
                                                                         score)
开发者ID:DaveYuan,项目名称:recommendersystem,代码行数:34,代码来源:test_forest.py

示例3: predictTitanic

# 需要导入模块: from sklearn.ensemble import RandomForestClassifier [as 别名]
# 或者: from sklearn.ensemble.RandomForestClassifier import score [as 别名]
def predictTitanic(train,test,predictors):

	#  Predictions using logistic regression

	logistic = linear_model.LogisticRegression()
	logistic.fit(train[predictors],train["Survived"])
	print " THe score for logistic regression is"
	print logistic.score(train[predictors],train["Survived"])


	# Predictions using scikit learn svm

	clf = svm.SVC()
	clf.fit(train[predictors],train["Survived"])
	print "THe score for SVM is"
	print clf.score(train[predictors],train["Survived"])
	predictions_svm = clf.predict(test[predictors])

	#Predictions using random forest models
	numEstimators = 100
 	model = RandomForestClassifier(numEstimators)
  	model.fit(train[predictors],train["Survived"])
	print "THe score for RF is"
	print model.score(train[predictors],train["Survived"])
	predictions_RFM = model.predict(test[predictors])


	submission = pd.DataFrame({
       "PassengerId": test["PassengerId"],
       "Survived": predictions_RFM
      })

	submission.to_csv('submission_RFM.csv', index=False)
开发者ID:arjun180,项目名称:Kaggle-Titanic-Machine-Learning,代码行数:35,代码来源:titanic_logic.py

示例4: run_random_forest

# 需要导入模块: from sklearn.ensemble import RandomForestClassifier [as 别名]
# 或者: from sklearn.ensemble.RandomForestClassifier import score [as 别名]
def run_random_forest(data, _max_depth):
    (feature_train, feature_test, label_train, label_test) = train_test_split(data[:, 0:-1], data[:, -1].astype(int),
                                                                              test_size=0.25)

    # TODO: Vary Number of Estimators
    rfc = RandomForestClassifier(n_estimators=500, criterion='gini', max_depth=_max_depth, max_features='auto',
                                     bootstrap=True, oob_score=True, n_jobs=4, verbose = 1)
    rfc.fit(feature_train, label_train)
    training_error = rfc.score(feature_train, label_train)
    #cross_validation_score = cross_val_score(rfc, feature_train, label_train, cv=10)
    testing_error = rfc.score(feature_test, label_test)
    out_of_bag_error = rfc.oob_score_

    print "Random Forest Results for Max Depth:", _max_depth
    print "Training Accuracy:", training_error
    #print "10-fold Cross Validation Accuracy: %0.2f (+/- %0.2f)" % (cross_validation_score.mean(), cross_validation_score.std() * 2)
    print "Testing Accuracy:", testing_error
    print "Out of Bag Accuracy:", out_of_bag_error

    feature_importance = rfc.feature_importances_
    stddev = np.std([tree.feature_importances_ for tree in rfc.estimators_], axis=0)
    indices = np.argsort(feature_importance)[::-1]

    # Print the feature ranking
    print("Feature ranking:")
    for f in range(len(feature_importance)):
        print("%d. feature %d (%f)" % (f + 1, indices[f], feature_importance[indices[f]]))

    plot_feature_importance(feature_importance, indices, stddev, "random-forest-feature-importance-depth-" + str(_max_depth))
开发者ID:BeifeiZhou,项目名称:social-network-recommendation,代码行数:31,代码来源:runClassifier.py

示例5: check_accuracy

# 需要导入模块: from sklearn.ensemble import RandomForestClassifier [as 别名]
# 或者: from sklearn.ensemble.RandomForestClassifier import score [as 别名]
def check_accuracy(X, y):
    X_train, X_test, y_train, y_test = \
        cross_validation.train_test_split(X, y, test_size=0.40, random_state=0)
    clf = RandomForestClassifier(n_estimators=100, max_depth=None)
    clf.fit(X_train, y_train)
    print "Score on test set1: ", clf.score(X_test, y_test)
    clf = ExtraTreesClassifier(n_estimators=100, max_depth=None, min_samples_split=1, random_state=0)
    clf.fit(X_train, y_train)
    print "Score on test set2: ", clf.score(X_test, y_test)

    # clf = MLPClassifier(algorithm='l-bfgs', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1)
    # clf.fit(X_train, y_train)
    # print "Score on test set: ", clf.score(X_test, y_test)

    clf = AdaBoostClassifier(n_estimators=100)
    clf.fit(X_train, y_train)
    print "Score on test set3 : ", clf.score(X_test, y_test)

    clf1 = ExtraTreesClassifier(n_estimators=50, max_depth=None, min_samples_split=1, random_state=0)
    clf2 = RandomForestClassifier(n_estimators=50, random_state=0)
    clf3 = AdaBoostClassifier(n_estimators=50)
    clf4 = GaussianNB()

    clf = VotingClassifier(estimators=[('et', clf1), ('rf', clf2), ('abc', clf3), ('gn', clf4)], voting='hard')
    clf.fit(X_train, y_train)
    print "Score on test set 4: ", clf.score(X_test, y_test)
开发者ID:bikash,项目名称:kaggleCompetition1,代码行数:28,代码来源:model_0.009858.py

示例6: randomforest_info

# 需要导入模块: from sklearn.ensemble import RandomForestClassifier [as 别名]
# 或者: from sklearn.ensemble.RandomForestClassifier import score [as 别名]
    def randomforest_info(self, max_trees = 1000, step = 40, k_folds = 5):
        print 'Characterising R_forest. Looping through trees: ',
        self.treedata = np.zeros((max_trees/step, 10))
        for i,n_trees in enumerate(np.arange(0, max_trees,step)):
            if n_trees == 0:
                n_trees = 1
            print n_trees,
            r_forest = RandomForestClassifier(n_estimators=n_trees, n_jobs=5, max_depth=None, min_samples_split=1, random_state=0)
            scores = cross_validation.cross_val_score(r_forest, self.iss_features, self.labels, cv=k_folds,n_jobs=5)
            r_forest_full = RandomForestClassifier(n_estimators=n_trees, n_jobs=5, max_depth=None, min_samples_split=1, random_state=0)
            r_forest_full.fit(self.iss_features,self.labels)
            self.treedata[i,0] = n_trees
            self.treedata[i,1] = scores.mean()
            self.treedata[i,2] = scores.std()
            # now add the test dataset - score
            self.treedata[i,3] = r_forest_full.score(self.iss_validation_features, self.validation_labels)

            r_forest_lda = RandomForestClassifier(n_estimators=n_trees, n_jobs=5, max_depth=None, min_samples_split=1, random_state=0)
            r_forest_lda_full = RandomForestClassifier(n_estimators=n_trees, n_jobs=5, max_depth=None, min_samples_split=1, random_state=0)
            r_forest_lda_full.fit(self.lda_iss_features,self.labels)
            lda_scores = cross_validation.cross_val_score(r_forest_lda, self.lda_iss_features, self.labels, cv=k_folds,n_jobs=5)
            self.treedata[i,4] = lda_scores.mean()
            self.treedata[i,5] = lda_scores.std()
            self.treedata[i,6] = r_forest_lda_full.score(self.lda_iss_validation_features, self.validation_labels)
            print self.treedata[i,6]

            r_forest_pca = RandomForestClassifier(n_estimators=n_trees, n_jobs=5, max_depth=None, min_samples_split=1, random_state=0)
            r_forest_pca_full = RandomForestClassifier(n_estimators=n_trees, n_jobs=5, max_depth=None, min_samples_split=1, random_state=0)
            r_forest_pca_full.fit(self.pca_iss_features,self.labels)
            pca_scores = cross_validation.cross_val_score(r_forest_pca, self.pca_iss_features, self.labels, cv=k_folds,n_jobs=5)
            self.treedata[i,7] = pca_scores.mean()
            self.treedata[i,8] = pca_scores.std()
            self.treedata[i,9] = r_forest_pca_full.score(self.pca_iss_validation_features, self.validation_labels)
开发者ID:rouzbeh,项目名称:networkclassifer,代码行数:35,代码来源:classifier.py

示例7: RF_results

# 需要导入模块: from sklearn.ensemble import RandomForestClassifier [as 别名]
# 或者: from sklearn.ensemble.RandomForestClassifier import score [as 别名]
def RF_results():
	print "--------------RandomForestClassifier-----------------"
	rang = [None, 10, 20, 50, 100, 200, 400]
	
	print "--------------With HOG-----------------"
	ans = []
	print "n_estimators	Accuracy"
	for i in rang:
		clf = RandomForestClassifier(n_estimators=i)
		clf.fit(X_train_hog, y_train)
		mean_accuracy = clf.score(X_test_hog, y_test)
		print i, "	", mean_accuracy
		ans.append('('+str(i)+", "+str(mean_accuracy)+')')
	print ans
	
	print "\n--------------Without HOG-----------------"
	ans = []
	print "n_estimators	Accuracy"
	for i in rang:
		clf = RandomForestClassifier(n_estimators=i)
		clf.fit(X_train, y_train)
		mean_accuracy = clf.score(X_test, y_test)
		print i, "	", mean_accuracy
		ans.append('('+str(i)+", "+str(mean_accuracy)+')')
	print ans
开发者ID:vickianand,项目名称:object-classification-for-surveillance,代码行数:27,代码来源:test_classifiers.py

示例8: cross_validation

# 需要导入模块: from sklearn.ensemble import RandomForestClassifier [as 别名]
# 或者: from sklearn.ensemble.RandomForestClassifier import score [as 别名]
def cross_validation(X, y):
    #fig = plt.figure()
    #ax = fig.add_subplot(111, projection='3d')
    assert(len(y) == len(X))
    # Split the dataset in two equal parts
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.8, random_state=42)
 
    depth = [8, 16, 32, 64]
    split = [1, 2, 4, 8, 16, 32, 64]
    best_score = 0 
    best_train_score = 0
    best_param = None
    for d in depth:
        for s in split:
            model = RandomForestClassifier(n_estimators=500, criterion="entropy", max_features="sqrt", max_depth=d, min_samples_split=s, n_jobs=-1)
            model = model.fit(X_train, y_train)
            print "Depth: %d  split: %d" % (d, s)
            print "Model trainning score:"
            score_train = model.score(X_train, y_train)
            print score_train
            #ax.scatter(d, s, score_train, c='b', marker='o')
            print "Model test score:"
            score_test = model.score(X_test, y_test)
            print score_test
            #ax.scatter(d, s, score_test, c='r', marker='^')
 
            if score_test > best_score:
                best_score = score_test
                best_train_score = score_train
                best_param = model.get_params()
    print "=================="
    print best_train_score
    print best_score
    print best_param
    return best_param
开发者ID:21zhouyun,项目名称:KaggleOCR,代码行数:37,代码来源:random_forest_util.py

示例9: randomforest

# 需要导入模块: from sklearn.ensemble import RandomForestClassifier [as 别名]
# 或者: from sklearn.ensemble.RandomForestClassifier import score [as 别名]
def randomforest():
    df = pd.read_csv("train.csv")
    cleanfloat(df)
    feature_columns_to_use = ['Pclass','Sex','Age','SibSp','Fare','Parch','Embarked']
    X = df[feature_columns_to_use]
    y = df['Survived'] 

    forest = RandomForestClassifier(n_estimators=100)
    forest = forest.fit( X,y )
    print forest.score(X, y)

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
    model2 = RandomForestClassifier(n_estimators = 100)
    model2.fit(X_train, y_train)
    predicted = model2.predict(X_test)
    print metrics.accuracy_score(y_test, predicted)


    dftest = pd.read_csv("test.csv")
    cleanfloat(dftest)
    X = dftest[feature_columns_to_use]
    output = forest.predict(X).astype(int)
    result = {'PassengerId':dftest.PassengerId, 'Survived':output}
    dfresult = pd.DataFrame(result)
    dfresult.to_csv("result.csv",index=False)
开发者ID:nplay007,项目名称:kaggle,代码行数:27,代码来源:myrandomforest.py

示例10: Model_RF

# 需要导入模块: from sklearn.ensemble import RandomForestClassifier [as 别名]
# 或者: from sklearn.ensemble.RandomForestClassifier import score [as 别名]
class Model_RF(object):
    def __init__(self,model,parameter = {"n_estimators" :10, "max_depth" :5, "max_features":10, "CV_size": 0}):
        self.train = model.train
        self.test = model.test
        self.CVsize = float(parameter["CV_size"].get())
        train = np.array(self.train)
        self.X_train = train[:, :-1]
        self.y_train = train[:, -1]
        self.X_train,self.X_CV,self.y_train,self.y_CV = train_test_split(self.X_train, self.y_train, test_size=self.CVsize)
        if self.CVsize == 0:
            self.clf = RandomForestClassifier(n_estimators = int(parameter["n_estimators"].get()), max_features = parameter["max_features"].get(), max_depth = int(parameter["max_depth"].get()))
        self.model = model
    
    def fit(self):
        self.clf.fit(self.X_train,self.y_train)

    def score(self):
        pre = self.clf.predict(self.X_train)
        truth = self.y_train
        print ("score: " + str(self.clf.score(self.X_train,truth)))
        print ("f1: " + str(f1_score(truth,pre, average=None)))
        print ("AUC score: " + str(roc_auc_score(truth,pre)))

    def save_results(self):
        pre = self.model.clf.predict(self.model.test)
        df = pd.DataFrame({"predict":pre})
        fileName = tkFileDialog.asksaveasfilename()
        df.to_csv(fileName)

    def crossValidation(self):
        estimatorList = [10,50,100,200,500]
        maxFeatList = ["sqrt","log2",None]
        bestScore = [0,0,None]
        bestF1ScoreNeg = [0,0,None]
        bestF1ScorePos = [0,0,None]
        for e in estimatorList:
            for maxFeat in maxFeatList:
                self.clf = RandomForestClassifier(n_estimators = e, max_features = maxFeat)
                self.clf.fit(self.X_train,self.y_train)
                pre = self.clf.predict(self.X_CV)
                truth = self.y_CV
                score = self.clf.score(self.X_CV,truth)
                if score > bestScore[0]:
                    bestScore[0] = score
                    bestScore[1] = e
                    bestScore[2] = maxFeat
                f1pos = f1_score(truth,pre, average=None)[1]
                if f1pos > bestF1ScorePos[0]:
                    bestF1ScorePos[0] = f1pos
                    bestF1ScorePos[1] = e
                    bestF1ScorePos[2] = maxFeat
                f1neg = f1_score(truth,pre, average=None)[0]
                if f1neg > bestF1ScoreNeg[0]:
                    bestF1ScoreNeg[0] = f1neg
                    bestF1ScoreNeg[1] = e
                    bestF1ScoreNeg[2] = maxFeat

        print ("Best [score,n_estimators,max_features] on Cross Validation set: " + str(bestScore))
        print ("Best [f1(pos),n_estimators,max_features] on Cross Validation set: " + str(bestF1ScorePos))
        print ("Best [f1(neg),n_estimators,max_features] on Cross Validation set" + str(bestF1ScoreNeg))
开发者ID:cndn,项目名称:sklearn-simpleGUI,代码行数:62,代码来源:skgui.py

示例11: randforpat

# 需要导入模块: from sklearn.ensemble import RandomForestClassifier [as 别名]
# 或者: from sklearn.ensemble.RandomForestClassifier import score [as 别名]
def randforpat():
    df = pd.read_csv("train.csv")
    cleanpatsy(df)
    y, X = dmatrices('Survived ~ Pclass + Sex + Age + SibSp + Parch + Fare + Cabin + Embarked',df, return_type="dataframe")
    y = np.ravel(y)


    forest = RandomForestClassifier(n_estimators=100)
    forest = forest.fit( X,y )
    print forest.score(X, y)



    # # evaluate the model by splitting into train and test sets
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
    model2 = RandomForestClassifier(n_estimators = 100)
    model2.fit(X_train, y_train)
    predicted = model2.predict(X_test)
    print metrics.accuracy_score(y_test, predicted)


    dftest = pd.read_csv("test.csv")
    cleanpatsy(dftest)
    X = dmatrix('Pclass + Sex + Age + SibSp + Parch + Fare + Cabin + Embarked',dftest, return_type="dataframe")
    output = forest.predict(X).astype(int)
    result = {'PassengerId':dftest.PassengerId, 'Survived':output}
    dfresult = pd.DataFrame(result)
    dfresult.to_csv("result.csv",index=False)
开发者ID:nplay007,项目名称:kaggle,代码行数:30,代码来源:myrandomforest.py

示例12: main

# 需要导入模块: from sklearn.ensemble import RandomForestClassifier [as 别名]
# 或者: from sklearn.ensemble.RandomForestClassifier import score [as 别名]
def main():
    train = csv_io.read_data("{}/Data/train.csv".format(os.getcwd()), True)

    target = [float(x[0]) for x in train]

    # Remove the target from the training
    train = [x[1:] for x in train]

    # Remove the categoricals that I can't convert
    for x in train:
        del x[1]
        del x[1]
        del x[5]
        del x[6]
    cats = preprocess.enum_categ_data(train, "f", 10)
    preprocess.strf_to_floats(train, missing="average")

    #    test = csv_io.read_data("{}/Data/test.csv".format(os.getcwd()), True)
    #
    #    # Remove the categoricals that I can't convert
    #    for x in test:
    #        del x[1]
    #        del x[1]
    #        del x[5]
    #        del x[6]

    # I can't just run enum_categ_data on test data, need to match the right cat to the right index!!!

    #    cats = preprocess.enum_categ_data(test, 'f', 10)
    #    preprocess.strf_to_floats(test, missing='average')

    rf = RandomForestClassifier(n_estimators=100, min_samples_split=2)
    rf.fit(train, target)

    print rf.score(train, target)
开发者ID:ab24v07,项目名称:titanicMining,代码行数:37,代码来源:randForLearner.py

示例13: RFclassifier

# 需要导入模块: from sklearn.ensemble import RandomForestClassifier [as 别名]
# 或者: from sklearn.ensemble.RandomForestClassifier import score [as 别名]
def RFclassifier(n,hog_train_x,train_y,hog_test_x,test_y):
    clf = RandomForestClassifier(n_estimators=n, n_jobs=-1)
    clf.fit(hog_train_x,train_y)

    print 'Accuracy for Random Forest classifier on test set with',n,'trees:'

    print clf.score(hog_test_x,test_y)
开发者ID:aayushmudgal,项目名称:CS771-MLT,代码行数:9,代码来源:makedata.py

示例14: main

# 需要导入模块: from sklearn.ensemble import RandomForestClassifier [as 别名]
# 或者: from sklearn.ensemble.RandomForestClassifier import score [as 别名]
def main():
    # 辞書の読み込み
    dictionary = corpus.get_dictionary(create_flg=False)
    # 記事の読み込み
    contents = corpus.get_contents()

    # 特徴抽出
    data_train = []
    label_train = []
    for file_name, content in contents.items():
        data_train.append(corpus.get_vector(dictionary, content))
        label_train.append(corpus.get_class_id(file_name))

    # 分類器
    estimator = RandomForestClassifier()

    # 学習
    estimator.fit(data_train, label_train)

    # 学習したデータを予測にかけてみる(ズルなので正答率高くないとおかしい)
    print("==== 学習データと予測データが一緒の場合")
    print(estimator.score(data_train, label_train))

    # 学習データと試験データに分けてみる
    data_train_s, data_test_s, label_train_s, label_test_s = train_test_split(data_train, label_train, test_size=0.5)

    # 分類器をもう一度定義
    estimator2 = RandomForestClassifier()

    # 学習
    estimator2.fit(data_train_s, label_train_s)
    print("==== 学習データと予測データが違う場合")
    print(estimator2.score(data_test_s, label_test_s))
开发者ID:ryan5500,项目名称:chainer-news-classification,代码行数:35,代码来源:estimation.py

示例15: TestRandForest

# 需要导入模块: from sklearn.ensemble import RandomForestClassifier [as 别名]
# 或者: from sklearn.ensemble.RandomForestClassifier import score [as 别名]
def TestRandForest(dat, lab):

    '''                                        
    This function finds the optimal parameters for the classifier                                        
    Parameters:                                                                                       
    ----------
    dat: numpy array with all records
    lab: numpy array with class labels of all records                                                        
    Returns:
    --------
    par: optimal parameters for the classifier 
    '''

    # RF parameters. Will choose one based on which does best on the validation set
    # n_estimators, max_features
    est = range(15, 41, 5)
    feat = range(2, 8, 1)
    par = [(e,f) for e in est for f in feat]

    # want to try different ensembles to get error bar on score
    num = 10
    seed = np.random.randint(1000000, size = num)
    valScore = np.zeros((num, len(par)))
    testScore = np.zeros((num, len(par)))

    for nv in xrange(0,num):

        print 'Ensemble:', nv + 1

        # split training data into train, validation, test (60, 20, 20)
        xTrain, xTmp, yTrain, yTmp = cross_validation.train_test_split(dat, lab, 
                                                                       test_size = 0.4, 
                                                                       random_state = seed[nv])
        xVal, xTest, yVal, yTest = cross_validation.train_test_split(xTmp, yTmp, 
                                                                     test_size = 0.5, 
                                                                     random_state = seed[nv])

        # now train RF for each parameter combination
        for i in xrange(0,len(par)):
        
            clf = RandomForestClassifier(n_estimators=par[i][0], 
                                         max_features = par[i][1], 
                                         min_samples_split = 1)
            clf = clf.fit(xTrain, yTrain)
            valScore[nv,i] = clf.score(xVal, yVal)
            testScore[nv,i] = clf.score(xTest, yTest)

    # Find optimal parameters
    tmp = np.argmax(np.mean(valScore, axis = 0))
    print
    print 'Optimal parameters (num_estimators, max_features):', par[tmp]
    print ('Mean | Std Score (Validation set):', np.mean(valScore, axis = 0)[tmp],
           '|', np.std(valScore, axis = 0)[tmp])
    print ('Mean | Std Score (Test set):', np.mean(testScore, axis = 0)[tmp],
           '|', np.std(testScore, axis = 0)[tmp])

    # Return optimal parameters
    return par[tmp]
开发者ID:pzukin,项目名称:Titanic,代码行数:60,代码来源:titanicRF.py


注:本文中的sklearn.ensemble.RandomForestClassifier.score方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。