数据分析成长之路

from sklearn.cluster import KMeanskmeans_model = KMeans(n_clusters = n) # n_clusters 指定聚为n类result = kmeans_model.fit(X) # result保存聚类出的类标签kmeans_model.labels_ # show cluster labelskmeans_model.c...

from sklearn.linear_model import LinearRegressionlm = LinearRegression(fit_intercept=True) # fit_intercrpt意为计算截距项lm.fit(x_train, y_train)lm.intercept_ # 显示截距项lm.coef_ # 显示所得系数...

# 随机森林模型建立与训练from sklearn.ensemble import RandomForestRegressor # 其他模型按需要选择即可rfr = RandomForestRegressor(n_estimators=100, max_depth=3, random_state=0) # 参数自己看帮助文档进行选择rfr.fit(x_train, y_train) #...

from sklearn.linear_model import LogisticRegression# 实例化模型lr = LogisticRegression(C=c_value, penalty='l1', solver='liblinear')# 训练模型lr.fit(x_train, y_train.values.ravel())# 模型预测y_predict = lr.pr...