Question

所以我发现LinearSVC在TPOT分类器中，我一直在使用它作为我的模型并得到相当不错的分数（sklearn分数为0.95）。

def process(stock):
  df = format_data(stock)
  df[['HSI Volume', 'HSI', stock]] = df[['HSI Volume', 'HSI', stock]].pct_change()

# shift future value to current date
  df[stock+'_future'] = df[stock].shift(-1)
  df.replace([-np.inf, np.inf], np.nan, inplace=True)
  df.dropna(inplace=True)
  df['class'] = list(map(create_labels, df[stock], df[stock+'_future']))
  X = np.array(df.drop(['class', stock+'_future'], 1)) # 1 = column
  # X = preprocessing.scale(X)
  y = np.array(df['class'])

  X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.2)

  tpot = TPOTClassifier(generations = 10, verbosity=2)
  fitting = tpot.fit(X_train, y_train)
  prediction = tpot.score(X_test, y_test)
  tpot.export('pipeline.py')
  return fitting, prediction

十代后：TPOT推荐GaussianNB，在sklearn评分中得分约为0.77。

Generation 1 - Current best internal CV score: 0.5322255571                     
Generation 2 - Current best internal CV score: 0.55453535828                    
Generation 3 - Current best internal CV score: 0.55453535828                    
Generation 4 - Current best internal CV score: 0.55453535828                    
Generation 5 - Current best internal CV score: 0.587469903893                   
Generation 6 - Current best internal CV score: 0.587469903893                   
Generation 7 - Current best internal CV score: 0.597194474469                   
Generation 8 - Current best internal CV score: 0.597194474469                   
Generation 9 - Current best internal CV score: 0.597194474469                   
Generation 10 - Current best internal CV score: 0.597194474469                  

Best pipeline: GaussianNB(RBFSampler(input_matrix, 0.22))
(None, 0.54637855142056824)

我很好奇为什么LinearSVC得分较高但TPOT不推荐。是因为评分机制不同，从而导致不同的最优分类器？

非常感谢你！

Answer 1

我个人的猜测是，tpot卡在局部最大值上，可能会尝试更改测试大小，做更多代或缩放数据可能会有所帮助。此外，你能重做TPOT，看看你是否得到相同的结果？（我的猜测是否定的，因为遗传优化因突变而不确定）

为什么TPOT推荐分类器的得分低于LinearSVC？

1 个答案: