3 梯度下降：重要引數max_iter

l2 = []
l2test = []
Xtrain, Xtest, Ytrain, Ytest = train_test_split(X,y,test_size=0.3,random_state=420)
for i in np.arange(1,201,10):
  lrl2 = LR(penalty="l2",solver="liblinear",C=0.9,max_iter=i)
  lrl2 = lrl2.fit(Xtrain,Ytrain)
  l2.append(accuracy_score(lrl2.predict(Xtrain),Ytrain))
  l2test.append(accuracy_score(lrl2.predict(Xtest),Ytest))
  
graph 
 
= [l2,l2test]
color = ["black","gray"]
label = ["L2","L2test"]
  
plt.figure(figsize=(20,5))
for i in range(len(graph)):
  plt.plot(np.arange(1,201,10),graph[i],color[i],label=label[i])
plt.legend(loc=4)
plt.xticks(np.arange(1,201,10))
plt.show()
#我們可以使用屬性.n_iter_來呼叫本次求解中真正實現的迭代次數
lr = LR(penalty="l2",solver="
 
liblinear",C=0.9,max_iter=300).fit(Xtrain,Ytrain)
lr.n_iter_

4 二元迴歸與多元迴歸：重要引數solver & multi_class

from sklearn.datasets import load_iris
iris = load_iris()
for multi_class in ('multinomial', 'ovr'):
  clf = LogisticRegression(solver='sag', max_iter=100, random_state=42,
               multi_class
 
=multi_class).fit(iris.data, iris.target) #列印兩種multi_class模式下的訓練分數
#%的用法，用%來代替列印的字串中，想由變數替換的部分。%.3f表示，保留三位小數的浮點數。%s表示，字串。
#字串後的%後使用元祖來容納變數，字串中有幾個%，元祖中就需要有幾個變數
  print("training score : %.3f (%s)" % (clf.score(iris.data, iris.target), 
multi_class))

5 樣本不平衡與引數class_weight