pred_y=model.predict(test_x) r=sm.r2_score(test_y,pred_y) print('normal:',r) model2=st.DecisionTreeRegressor(max_depth=4) model3=se.AdaBoostRegressor(model2,n_estimators=400,random_state=7) model3.fit(train_x,train_y) pred_test_y_2=model3.predict(test_x) r=sm.r2_score(test...
随机选择条件进行建树,建立多个决策树形成森林。 决策树:进行选择或者预测的走向模型(树结构) 熵值公式:用于描述对选择的混乱程度。最常用的度量纯度的指标 熵值越小说明样本越纯,熵值越大,说明样本越混乱。函数与熵值对应关系的解释 熵值:熵值代表着混乱的程度,熵差代表信息增益(对决策的贡献程度) 剪枝 剪枝的目的其...
Random Forest 模型是一种基于集成学习的决策树算法。与传统的决策树算法不同,它不是通过一个单一的决策树来划分数据集,而是通过集成多个决策树来进行分类或回归,最终结果选择分类或回归的众数或平均数。随机森林在解决分类和回归问题中表现出色,具有很高的准确度和稳定的性能。 随机森林的优点包括: 随机森林采用多个决...
println("Learned classification forest model:\n" + model.toDebugString) 下面的例子用于回归。 import org.apache.spark.mllib.tree.RandomForest import org.apache.spark.mllib.tree.model.RandomForestModel import org.apache.spark.mllib.util.MLUtils // Load and parse the data file. val data = MLU...
# 训练随机森林 model = RandomForestClassifier(n_trees=100, max_depth=5, min_samples_split=2, random_state=seed_value) model.fit(X_train, y_train) 4.7 打印结果 模型训练完成之后可以使用如下代码查看训练集和测试集的准确率,如果有能力小伙伴可以绘制AUC曲线等查看模型效果。 # 结果 y_train_pred...
RandomForest model.//Empty categoricalFeaturesInfo indicatesallfeatures are continuous.val numClasses=2val categoricalFeaturesInfo=Map[Int,Int]()val numTrees=3//Use moreinpractice.val featureSubsetStrategy="auto"//Let the algorithm choose.val impurity="gini"val maxDepth=4val maxBins=32val model=...
LabeledPoint(-1.0, (6,[1,3,5],[4.0,5.0,6.0]))'''#Split the data into training and test sets (30% held out for testing) 分割数据集,留30%作为测试集(trainingData, testData) = data.randomSplit([0.7, 0.3])#Train a RandomForest model. 训练决策树模型#Empty categoricalFeaturesInfo indicat...
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor from sklearn.datasets import make_classification, load_iris from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score import numpy as np ```### 2. 创建数据集 对于分类问题，可以使用`make_...
Random forests function well with elevated-dimensional data because it is possible to work with chunks of data. Furthermore, when dealing with a subgroup of characteristics in the random forest model, it is easier to learn than applying decision trees, which may easily handle several features....
Furthermore, the model performances were also compared with those of well-known elasticity-based and double-mass curve methods, and the results of these models are approximate in the investigated basins, which implies that the random forest model has the potential for runoff simulation an...