本文实例为大家分享了python sklearn分类算法模型调用的具体代码，供大家参考，具体内容如下

实现对'NB','KNN','LR','RF','DT','SVM','SVMCV','GBDT'模型的简单调用。

# coding=gbk import time from sklearn import metrics import pickle as pickle import pandas as pd # Multinomial Naive Bayes Classifier def naive_bayes_classifier(train_x, train_y): from sklearn.naive_bayes import MultinomialNB model = MultinomialNB(alpha=0.01) model.fit(train_x, train_y) return model # KNN Classifier def knn_classifier(train_x, train_y): from sklearn.neighbors import KNeighborsClassifier model = KNeighborsClassifier() model.fit(train_x, train_y) return model # Logistic Regression Classifier def logistic_regression_classifier(train_x, train_y): from sklearn.linear_model import LogisticRegression model = LogisticRegression(penalty='l2') model.fit(train_x, train_y) return model # Random Forest Classifier def random_forest_classifier(train_x, train_y): from sklearn.ensemble import RandomForestClassifier model = RandomForestClassifier(n_estimators=8) model.fit(train_x, train_y) return model # Decision Tree Classifier def decision_tree_classifier(train_x, train_y): from sklearn import tree model = tree.DecisionTreeClassifier() model.fit(train_x, train_y) return model # GBDT(Gradient Boosting Decision Tree) Classifier def gradient_boosting_classifier(train_x, train_y): from sklearn.ensemble import GradientBoostingClassifier model = GradientBoostingClassifier(n_estimators=200) model.fit(train_x, train_y) return model # SVM Classifier def svm_classifier(train_x, train_y): from sklearn.svm import SVC model = SVC(kernel='rbf', probability=True) model.fit(train_x, train_y) return model # SVM Classifier using cross validation def svm_cross_validation(train_x, train_y): from sklearn.grid_search import GridSearchCV from sklearn.svm import SVC model = SVC(kernel='rbf', probability=True) param_grid = {'C': [1e-3, 1e-2, 1e-1, 1, 10, 100, 1000], 'gamma': [0.001, 0.0001]} grid_search = GridSearchCV(model, param_grid, n_jobs = 1, verbose=1) grid_search.fit(train_x, train_y) best_parameters = grid_search.best_estimator_.get_params() for para, val in list(best_parameters.items()): print(para, val) model = SVC(kernel='rbf', C=best_parameters['C'], gamma=best_parameters['gamma'], probability=True) model.fit(train_x, train_y) return model def read_data(data_file): data = pd.read_csv(data_file) train = data[:int(len(data)*0.9)] test = data[int(len(data)*0.9):] train_y = train.label train_x = train.drop('label', axis=1) test_y = test.label test_x = test.drop('label', axis=1) return train_x, train_y, test_x, test_y if __name__ == '__main__': data_file = "H:\\Research\\data\\trainCG.csv" thresh = 0.5 model_save_file = None model_save = {} test_classifiers = ['NB', 'KNN', 'LR', 'RF', 'DT', 'SVM','SVMCV', 'GBDT'] classifiers = {'NB':naive_bayes_classifier, 'KNN':knn_classifier, 'LR':logistic_regression_classifier, 'RF':random_forest_classifier, 'DT':decision_tree_classifier, 'SVM':svm_classifier, 'SVMCV':svm_cross_validation, 'GBDT':gradient_boosting_classifier } print('reading training and testing data...') train_x, train_y, test_x, test_y = read_data(data_file) for classifier in test_classifiers: print('******************* %s ********************' % classifier) start_time = time.time() model = classifiers[classifier](train_x, train_y) print('training took %fs!' % (time.time() - start_time)) predict = model.predict(test_x) if model_save_file != None: model_save[classifier] = model precision = metrics.precision_score(test_y, predict) recall = metrics.recall_score(test_y, predict) print('precision: %.2f%%, recall: %.2f%%' % (100 * precision, 100 * recall)) accuracy = metrics.accuracy_score(test_y, predict) print('accuracy: %.2f%%' % (100 * accuracy)) if model_save_file != None: pickle.dump(model_save, open(model_save_file, 'wb'))

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持。