酵母菌數(shù)據(jù)分類

數(shù)據(jù)挖掘技術(shù)與應(yīng)用酵母菌數(shù)據(jù)分類實訓(xùn)實訓(xùn)目標掌握KNN算法的基本原理和核心步驟。能應(yīng)用KNN算法解決實際問題。實訓(xùn)環(huán)境使用3.6版本的Python。使用jupyternotebook或PyCharm2018社區(qū)版作為代碼編輯器。實訓(xùn)數(shù)據(jù)yeast（酵母菌）數(shù)據(jù)集是一個多標簽數(shù)據(jù)集，里面包括14種標簽，103個特征值實訓(xùn)內(nèi)容導(dǎo)入必要的庫。導(dǎo)入數(shù)據(jù)集。獲取特征值。獲取標簽。查看誤差效果誤差值隨著k的取值變化隨機劃分訓(xùn)練集和測試集創(chuàng)建模型對測試集進行預(yù)測酵母菌數(shù)據(jù)分類實現(xiàn)代碼導(dǎo)入數(shù)據(jù)包fromsklearn.preprocessingimportLabelEncoderimportpandasaspdfromsklearn.model_selectionimporttrain_test_split,cross_val_scoreimportmatplotlib.pyplotaspltfromsklearn.neighborsimportKNeighborsClassifierimportnumpyasnp數(shù)據(jù)說明：yeast（酵母菌）數(shù)據(jù)集是一個多標簽數(shù)據(jù)集，里面包括14種標簽，103個特征值導(dǎo)入數(shù)據(jù)集df=pd.read_csv(r'data\yeast.csv')獲取特征值X=df.iloc[:,0:102]獲取標簽Y=df.iloc[:,103]取不同的領(lǐng)域數(shù)量，k=1到k=10，查看誤差效果k_range=range(1,11)k_error=[]forkink_range:knn=KNeighborsClassifier(n_neighbors=k)#通過cv參數(shù)決定數(shù)據(jù)集劃分比例，如按照5:1劃分訓(xùn)練集和測試集，cv取值為6scores=cross_val_score(knn,X,Y,cv=6,scoring='accuracy')k_error.append(1-scores.mean())用圖展示，誤差值隨著k的取值變化plt.plot(k_range,k_error)plt.xlabel('K')plt.ylabel('Error')plt.show()隨機劃分訓(xùn)練集和測試集X_train,X_test,y_train,y_test=train_test_split(X,Y,test_size=0.3,random_state=6)創(chuàng)建模型,并設(shè)置“鄰居”的數(shù)目為5knn=KNeighborsClassifier(n_neighbors=5)knn.fit(X_train,y_train)KNeighborsClassifier(algorithm='auto',leaf_size=30,metric='minkowski',metric_params=None,n_jobs=1,n_neighbors=5,p=2,weights='uniform')對測試集進行預(yù)測，并將結(jié)果與label進行對比，計算準確度y_pred=knn.predict(X_test)print("準確度為：{:.3f}".format(np.mean(y_p