Logistic regression 与sklearn的实现有一些不同。 Gradient Stochastic gradient descent algorithm importnumpyasnpfromsklearn.datasetsimportload_breast_cancerfromsklearn.model_selectionimportKFoldfromsklearn.metricsimportf1_score,recall_score,precision_score# evaluation metricsclasslogreg_sgd_clf:def__init__(sel...
Since the algorithm for logistic regression is very similar to the equation for linear regression, it forms part of a family of models called "generalized linear models". This is why logistic regression has "regression" in its name, even though it is a classification model. The sigmoid function...
读取数据并展示 path = r'E:\Master\ML\homework\02 Logistic Regression\ex2data1.txt' #names添加列名,header用指定的行来作为标题,对无表头的数据,则需设置header=None,否则第一行数据被作为表头 data = pd.read_csv(path,header=None,names=['Exam1','Exam2','Admitted']) data.head() print(data.h...
模型评估(Model Evaluation)模型评估常用的包括了上面提到的交叉验证,损失函数,正则化惩罚,混淆矩阵,FPR,TPR(也可以称为recall),ROC,AUC,F1-Score下面不仅做了一次5层的交叉验证,还用了用了L1惩罚项,不断用惩罚系数去调整模型的精确度,确认惩罚系数,从结果图中可以看到,模型精确度不断在提升。 # 交叉验证准备,切...
fromsklearn.metricsimportroc_curve,auc# y = np.array([1,1,2,2])# pred = np.array([0.1, 0.4, 0.35, 0.8])fpr,tpr,thresholds=roc_curve(y,pred,pos_label=1)fpr# array([ 0. , 0.5, 0.5, 1. ])tpr# array([ 0.5, 0.5, 1. , 1. ])thresholds#array([ 0.8 , 0.4 , 0.35, 0.1...
grid_search = GridSearchCV(estimator = LogisticRegression(penalty="l2",solver="liblinear"), # penalty:正则项,防止模型过拟合,可选择l1或l2;solver:优化算法,根据数据量可选择liblinear、sag/saga、lbfgs param_grid = param_set,cv=kflod,n_jobs=-1) ...
import org.apache.spark.mllib.evaluation.MulticlassMetrics import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.util.MLUtils /** * Created by xubo on 2016/5/23. * 一元逻辑回归 */ object LogisticRegressionWithLDFGS { ...
predictionAndLabels = test.map(predicTest)frompyspark.mllib.evaluationimportMulticlassMetrics#accuracymetrics = MulticlassMetrics(predictionAndLabels) accuracy = metrics.accuracy accuracy# plot boundaryimportnumpyasnp## meshgridx0, x1 = np.meshgrid( ...
Chinchor N, and Sundheim BM (1993) MUC-5 evaluation metrics. In: Fifth message understanding conference (MUC-5): proceedings of a conference held in Baltimore, Maryland, August 25–27, 1993 Chung J, Kim H (2019) Crime risk maps: a multivariate spatial analysis of crime data. Geogr Anal...
# Step 1: Import packages, functions, and classes import numpy as np from sklearn.linear_model import LogisticRegression from sklearn.metrics import classification_report, confusion_matrix # Step 2: Get data x = np.arange(10).reshape(-1, 1) y = np.array([0, 1, 0, 0, 1, 1, 1,...