我想在基于sklearn库的Titanic ML解决方案中实现梯度提升分类器。
我在Ubuntu 18.04上使用VS Code。
我尝试过:
# Splitting the Training Data
from sklearn.model_selection import train_test_split
predictors = train.drop(['Survived', 'PassengerId'], axis=1)
target = train["Survived"]
x_train, x_val, y_train, y_val = train_test_split(predictors,
target, test_size = 0.22, random_state = 0)
# Gradient Boosting Classifier
from sklearn.ensemble import GradientBoostingClassifier
gbk = GradientBoostingClassifier()
gbk.fit(x_train, y_train)
..它返回:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/sj/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/gradient_boosting.py", line 1395, in fit
X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'], dtype=DTYPE)
File "/home/sj/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py", line 756, in check_X_y
estimator=estimator)
File "/home/sj/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py", line 527, in check_array
array = np.asarray(array, dtype=dtype, order=order)
File "/home/sj/anaconda3/lib/python3.7/site-packages/numpy/core/numeric.py", line 501, in asarray
return array(a, dtype, copy=False, order=order)
ValueError: could not convert string to float: 'Baby'
我们将不胜感激。我是DS的新手。
答案 0 :(得分:0)
我认为您的火车数据中可能没有数值。您的分类器可以采用数字输入。这就是为什么它尝试将此处'Baby'
的字符串转换为浮点数的原因。由于不支持此操作,因此失败。
也许再次查看您的数据。