ValueError: continuous is not supported with xgboost classification
ValueError: continuous is not supported with xgboost classification
这是我的错误
ValueError Traceback (most recent call last)
<ipython-input-5-7c13d55b8367> in <module>()
1 from sklearn.metrics import confusion_matrix, accuracy_score
2 y_pred = classifier.predict(X_test)
----> 3 cm = confusion_matrix(y_test, y_pred)
4 print(cm)
5 accuracy_score(y_test, y_pred)
第二帧
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py in _check_targets(y_true, y_pred)
95 # No metrics support "multiclass-multioutput" format
96 if (y_type not in ["binary", "multiclass", "multilabel-indicator"]):
---> 97 raise ValueError("{0} is not supported".format(y_type))
98
99 if y_type in ["binary", "multiclass"]:
ValueError: continuous is not supported
这是我的代码
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('NBA_proj_14.csv')
X = dataset.iloc[:, :-13].values
y = dataset.iloc[:, -13].values
将数据集拆分为训练集和测试集
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
在训练集上训练 XGBoost
from xgboost import XGBClassifier
classifier = XGBClassifier()
classifier.fit(X_train, y_train)
制作混淆矩阵
from sklearn.metrics import confusion_matrix, accuracy_score
y_pred = classifier.predict(X_test)
cm = confusion_matrix(y_test, y_pred)
print(cm)
accuracy_score(y_test, y_pred)```
这是我的数据集
这里:
X = dataset.iloc[:, :-13].values
y = dataset.iloc[:, -13].values
您不是构建特征数组 X
和目标数组 y
,而是按行拆分数据集,这不是您想要的。
您一个人知道 what/where 您想要预测的 class 是您想要制作目标数组的。正如错误所暗示的那样,在进行 class 化、构建混淆矩阵时,您不应预测连续变量。
这是我的错误
ValueError Traceback (most recent call last)
<ipython-input-5-7c13d55b8367> in <module>()
1 from sklearn.metrics import confusion_matrix, accuracy_score
2 y_pred = classifier.predict(X_test)
----> 3 cm = confusion_matrix(y_test, y_pred)
4 print(cm)
5 accuracy_score(y_test, y_pred)
第二帧
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py in _check_targets(y_true, y_pred)
95 # No metrics support "multiclass-multioutput" format
96 if (y_type not in ["binary", "multiclass", "multilabel-indicator"]):
---> 97 raise ValueError("{0} is not supported".format(y_type))
98
99 if y_type in ["binary", "multiclass"]:
ValueError: continuous is not supported
这是我的代码
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('NBA_proj_14.csv')
X = dataset.iloc[:, :-13].values
y = dataset.iloc[:, -13].values
将数据集拆分为训练集和测试集
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
在训练集上训练 XGBoost
from xgboost import XGBClassifier
classifier = XGBClassifier()
classifier.fit(X_train, y_train)
制作混淆矩阵
from sklearn.metrics import confusion_matrix, accuracy_score
y_pred = classifier.predict(X_test)
cm = confusion_matrix(y_test, y_pred)
print(cm)
accuracy_score(y_test, y_pred)```
这是我的数据集
这里:
X = dataset.iloc[:, :-13].values
y = dataset.iloc[:, -13].values
您不是构建特征数组 X
和目标数组 y
,而是按行拆分数据集,这不是您想要的。
您一个人知道 what/where 您想要预测的 class 是您想要制作目标数组的。正如错误所暗示的那样,在进行 class 化、构建混淆矩阵时,您不应预测连续变量。