I'm building a Random Forest Binary Classsifier in python on a pre-processed dataset with 4898 instances, 60-40 stratified split-ratio and 78% data belonging to one target label and the rest to the other. What value of n_estimators should I choose in order to achieve the most practically...
在看到precision_recall_curve之后,如果我想设置阈值= 0.4,如何在我的随机森林模型(二进制分类)中实现0.4,对于任何概率<0.4,将其标记为0,对于任何>=0.4,将其标记为1from sklearn.ensemble import RandomForestClassifier random_forest = RandomForestClassifier(n_estimators 浏览0提问于2018-04-12得票数 9 回答...
forest = ExtraTreesClassifier(n_estimators=250, random_state=0) forest.fit(X, y) for n, (name, estimator) in enumerate(estimators): estimator.fit(X, y) importances = estimator.feature_importances_ std = np.std([tree.feature_importances_ for tree in forest.estimators_], axis=0) indices...
confusion_matrixes = {} for n_estimator in n_estimator_params:rf = RandomForestClassifier(n_estimators=n_estimator,n_jobs=-1, verbose=True)rf.fit(X[training], y[training])print ("Accuracy:\t", (rf.predict(X[~training]) == y[~training]).mean())'''=== RESTART: E:/python/pp138....
RandomForestClassifier 重要参数: criterion:不纯度衡量指标 max_depth:树的最大深度,超过最大深度会被剪掉 n_estimators :这是随机森林的数量,即评估器的数量,n_estimators 越大,模型效果越好(任何模型都有决策边界) random_state:用来设置分枝中的随机模式的参数,默认为None; ...
rf= RandomForestClassifier(n_estimators=n_estimator,n_jobs=-1, verbose=True) rf.fit(X[training], y[training])print("Accuracy:\t", (rf.predict(X[~training]) == y[~training]).mean())'''=== RESTART: E:/python/pp138.py === [Parallel(n_jobs=-1)]: Done 1 out of 1 | elapsed...
ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score scores =[] for k in range(1, 200): rfc = RandomForestClassifier(n_estimators=k) rfc.fit(x_train, y_train) y_pred = rfc.predict(x_test) scores.append(accuracy_score(y_test, y_pred)) import matplotlib....
# 1-200颗树的学习曲线superpa = []foriinrange(200): rfc = RandomForestClassifier(n_estimators=i+1, n_jobs=-1) rfc_cross = cross_val_score(rfc, wine.data, wine.target, cv=10).mean() superpa.append(rfc_cross)print(max(superpa), superpa.index(max(superpa))) ...
示例1: RandomForestClassifier ▲点赞 7▼ # 需要导入模块: from sklearn.ensemble import RandomForestClassifier [as 别名]# 或者: from sklearn.ensemble.RandomForestClassifier importn_estimators[as 别名]model = RandomForestClassifier(n_jobs=6)ifargs.CV: ...
Asymptotic Normality for Multivariate Random Forest EstimatorsKevin Li