Python RandomForestRegressor.fit方法代码示例

2023-03-27 21:11| 来源: 网络整理| 查看: 265

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

在下文中一共展示了RandomForestRegressor.fit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，您的评价将有助于我们的系统推荐出更棒的Python代码示例。

示例1: main # 需要导入模块: from sklearn.ensemble import RandomForestRegressor [as 别名] # 或者: from sklearn.ensemble.RandomForestRegressor import fit [as 别名] def main(): fi = open('25-75_microcap_list.txt', 'r') symbols = [] for i in fi: symbols.append(i.strip()) #symbols = symbols[0:6] train, test = get_data(symbols, n = 30, flag = 1, blag = 12) train = train.replace([np.inf, -np.inf], np.nan) test = test.replace([np.inf, -np.inf], np.nan) train = train.dropna(axis=0) test = test.dropna(axis=0) print 'Fitting\n' m = RandomForestRegressor(n_estimators=250, n_jobs=1) m.fit(train.ix[:,6:], train.ix[:,5]) print 'Predicting\n' preds = m.predict(test.ix[:,5:]) result = test.ix[:,:4] result['Prediction'] = preds result = result.sort('Prediction', ascending=False) print result.head() result.to_csv('trade_result.csv', sep = ',', index = False)开发者ID:iswdp，项目名称:microcap，代码行数:28，代码来源:trade.py 示例2: train_sklearn_forest # 需要导入模块: from sklearn.ensemble import RandomForestRegressor [as 别名] # 或者: from sklearn.ensemble.RandomForestRegressor import fit [as 别名] def train_sklearn_forest(XAlltr, XAllcv, yAlltr, yAllcv, trees=20): errors = [] models = [] X = XAlltr Xcv = XAllcv print "training sklearn forset" for feature in range(np.shape(yAlltr)[1]): y = yAlltr[:, feature] ycv = yAllcv[:, feature] # train a random forest with different number of trees and plot error # print "training forest %d" % trees clf = RandomForestRegressor(n_estimators=trees, min_samples_leaf=30, max_depth=20) clf = RandomForestRegressor(n_estimators=trees) clf.fit(X, y) pred = clf.predict(X) err = pred_error(y, pred, feature) predcv = clf.predict(Xcv) errcv = pred_error(ycv, predcv, feature) print [trees, feature, err, errcv] errors.append((trees, feature, err, errcv)) models.append(clf) return models, errors开发者ID:Schmiddi，项目名称:Hand_Pose_Estimation，代码行数:33，代码来源:train_second_phase.py 示例3: buildTreeRegressor # 需要导入模块: from sklearn.ensemble import RandomForestRegressor [as 别名] # 或者: from sklearn.ensemble.RandomForestRegressor import fit [as 别名] def buildTreeRegressor(predictorColumns, structurestable = 'structures.csv', targetcolumn = 'c_a', md = None): """ Build a random forest-regressor model to predict some structure feature from compositional data. Will return the model trained on all data, a mean_absolute_error score, and a table of true vs. predicted values """ df = pd.read_csv(structurestable) df = df.dropna() if('fracNobleGas' in df.columns): df = df[df['fracNobleGas']

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