逻辑回归
##逻辑回归 import numpy as np import pandas as pd from sklearn.datasets import load_iris from sklearn.linear_model import LogisticRegression #1.导入数据 #data = pd.read_csv(’’) #2.数据预处理 #略,最终生成x_train,y_train,x_test,y_test #此处导入鸢尾花数据 x_train, y_train = load_iris(return_X_y=True) #3.模型训练 clf = LogisticRegression(random_state=0, solver=‘lbfgs’, multi_class=‘multinomial’) clf.fit(x_train, y_train) #4.模型预测 y_predict = clf.predict(x_train[:2, :]) print(y_predict) #参数列表与调参方法 LogisticRegression(penalty=’l2’, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=None, random