示例11: build_extra_tree_regressor ▲點讚 1▼ defbuild_extra_tree_regressor(X_test, X_train_full, y_train_full):print"Building ExtraTrees regressor..."etr =ExtraTreesRegressor(n_estimators=500) etr.fit(X_train_full, y_train_full) etr_predict = etr.predict(X_test)returnetr_predict 開發...
The Extra Tree Regressor (ETR) is used taking the ore grade, influent flow rate, the influent concentration, the feed granularity, inflation volume, froth stability and froth movement speed in flotation process as input variables, to establish forecasting model, and...
# 需要导入模块: from sklearn import tree [as 别名]# 或者: from sklearn.tree importExtraTreeRegressor[as 别名]deftest_objectmapper(self):df = pdml.ModelFrame([]) self.assertIs(df.tree.DecisionTreeClassifier, tree.DecisionTreeClassifier) self.assertIs(df.tree.DecisionTreeRegressor, tree.Decision...
1))40y_test = ss_y.transform(y_test.reshape(-1, 1))4142#4 三种集成回归模型进行训练和预测43#随机森林回归44rfr =RandomForestRegressor()45#训练46rfr.fit(x_train, y_train)47#预测 保存预测结果48rfr_y_predict =rfr.predict(x_test
Acoustic impedance prediction based on extended seismic attributes using multilayer perceptron, random forest, and extra tree regressor algorithmsAcoustic impedanceSeismic attributesMLPRRFRETRAcoustic impedance is the product of the density of a material and the speed at which an acoustic wave travels ...
model =ExtraTreesRegressor(**self.INITPARAMS) model.fit(X, y) self.model = model 开发者ID:DJRumble,项目名称:S2DS,代码行数:7,代码来源:estimator.py 示例2: do_etrees ▲点赞 6▼ defdo_etrees(filename):df, Y = create_merged_dataset(filename) ...
# 需要导入模块: from sklearn.ensemble import ExtraTreesRegressor [as 别名]# 或者: from sklearn.ensemble.ExtraTreesRegressor importfit[as 别名]deffit(self, X, y, weights = None, **kwargs):ifweightsisNone: weights = np.ones(y.shape[0]) ...
# 需要导入模块: from sklearn.ensemble import ExtraTreesRegressor [as 别名]# 或者: from sklearn.ensemble.ExtraTreesRegressor importpredict[as 别名]deftrain(self, verbose=False, training_data=None):n_estimators =50n_samples =5000trainingDataDict = self._getTrainingData(numSamples=n_samples) ...
ExtraTreesClassifier,ExtraTreesRegressor]forEstimatorinall_estimators: est = Estimator(min_impurity_decrease=0.1) est.fit(X, y)fortreeinest.estimators_:# Simply check if the parameter is passed on correctly. Tree tests# will suffice for the actual working of this paramassert_equal(tree.min_impuri...
The ETR algorithm initiates at the root node and progressively determines split rules by leveraging a randomized subset of features and a partially random cut point in each decision tree regressor. This iterative process is perpetuated at each subsequent child node until the traversal reaches a leaf...