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【机器学习】Ensemble Learning 集成学习 + Python代码实战

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By Extra TreesClassifier
| CSDN
2.1 随机森林 2.1.1 随机森林介绍 2.2.1 随机森林优势 能够处理高维度数据，并且不用做特征选择 训练完后，它能够给出哪些特征比较重要 容易并行化，速度较快 可以进行可视化展示，方便分析 2.2 KNN 2.3 软投票 2.4 硬投票 2.5 Bagging模型实战 2.5.1 构建实验数据集 from sklearn.model_selection import train_test_split from sklearn.datasets import make_moons import matplotlib.pyplot as plt X, y = make_moons(n_samples=500, noise=0.30, random_state=42) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42) plt.plot(X[:, 0][y == 0], X[:, 1][y == 0], 'yo', alpha=0.6) plt.plot(X[:, 0][y ==...
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Mental Health Prediction Using Machine Learning

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By Label Encoder, Aarti Parekh, Extra TreesClassifier, AdaBoost Classifier
| Analytics Vidhya
Intermediate Machine Learning Python This article was published as a part of the Data Science Blogathon. Technology is evolving round the clock in recent timess resulted in job opportunities for people all around the world. It comes with a hectic schedule that can be detrimental to people’s mental health. So During the Covid-19 panealth has been one of the most prominent issues, with stress, loneliness, and depression all on the rise over the last year.
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