KNN&SVM&KMEANS&RFE

代码说明：

说明：  1,載入iris 數劇集並設置提取60%的數據做為訓練集，40%數據做為測試集執行KNN 分類算法，並執行五次的交叉驗證（CV）和顯示準確率和混淆矩陣，並找出最佳K值。 2,載入iris 數劇集並設置提取60%的數據做為訓練集，40%數據做為測試集,執行支持向量機（SVM） 算法，並執行五次的交叉驗證（CV）和顯示準確率和混淆矩陣。 3,載入iris 數據集並執行KMEANS 聚類算法家數具分成五個群體，顯示準確率 4,載入iris 數據集執行線性迴歸算法，並利用特徵萃取（Feature Extraction）中的Recursive feature elimination (RFE)對iris 數據集中的特徵欄位進行重要性排序(1. Load iris series and set 60% data as training set and 40% data as test set to implement KNN classification algorithm, and perform five times of cross validation (CV) and display accuracy and confusion matrix, and find out the best K value. 2. Load iris series and set 60% data as training set and 40% data as test set, execute support vector machine (SVM) algorithm, and perform five times of cross validation (CV) and display accuracy and confusion matrix. 3. Loading iris data set and executing kmeans clustering algorithm, the number of users is divided into five groups, showing the accuracy rate 4. Load iris data set, execute linear regression algorithm, and use recursive feature elimination (RFE) in feature extraction to sort the importance of feature fields in iris dataset)

文件列表：

iris.csv, 3817 , 2020-05-29
KNN&SVM&KMEANS&RFE.py, 7279 , 2020-07-04