计算基尼指数的准确性？

问题描述

我正在从头开始构建决策树，而不使用 sklibrary。我目前用来分割样本的方法是 K 折法。我想从 K-fold 方法切换到 train_test_split 方法。我如何计算准确度？我已经完成了预测部分。

我的 K 折方法

def cross_validation_split(dataset,n_folds):
dataset_split = list()
dataset_copy = list(dataset)
fold_size = int(len(dataset) / n_folds)
for i in range(n_folds):
    fold = list()
    while len(fold) < fold_size:
        index = randrange(len(dataset_copy))
        fold.append(dataset_copy.pop(index))
    dataset_split.append(fold)
return dataset_split


# Calculate accuracy percentage
def accuracy_metric(actual,predicted):
    correct = 0
    for i in range(len(actual)):
        if actual[i] == predicted[i]:
            correct += 1
    return correct / float(len(actual)) * 100.0
 
# Evaluate an algorithm using a cross validation split
def evaluate_algorithm(dataset,algorithm,n_folds,*args):      
    folds = cross_validation_split(dataset,n_folds)
    scores = list()
    for fold in folds:
        train_set = list(folds)
        train_set.remove(fold)
        train_set = sum(train_set,[])
        test_set = list()
        for row in fold:
            row_copy = list(row)
            test_set.append(row_copy)
            row_copy[-1] = None
        predicted = algorithm(train_set,test_set,*args)
        actual = [row[-1] for row in fold]
        accuracy = accuracy_metric(actual,predicted)
        scores.append(accuracy)
    return scores

我的 train_test_split 方法

def train_test_split(X,y,test_size=0.33):
    i = int((1 - test_size) * X.shape[0]) + 1
    X_train,X_test = np.split(X,[i])
    y_train,y_test = np.split(y,[i])
    return X_train,X_test,y_train,y_test

数据是如何拆分的

df = pd.read_csv("corrected.csv")

df = df.sample(frac=0.33,random_state=255,replace=True)

data = df.to_numpy()
X = data[:,:-1] 
y = data[:,-1] - 1 

X_train,y_test = train_test_split(X,y)

决策树

def decision_tree(train,test,max_depth,min_size):
    tree = build_tree(train,min_size)
    predictions = list()
    for row in test:
        prediction = predict(tree,row)
        predictions.append(prediction)
    return(predictions)
        

解决方法

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