예시문제 작업형2(신버전) 질문있습니다

2023-12-01T05:51:16.835Z

0

import pandas as pd
train = pd.read_csv("data/customer_train.csv")
test = pd.read_csv("data/customer_test.csv")

# 전처리
train['환불금액'] = train['환불금액'].fillna(0)
test['환불금액'] = test['환불금액'].fillna(0)

cols = train.select_dtypes(include='object').columns

train = pd.get_dummies(train, columns = cols)
test = pd.get_dummies(test, columns = cols)

target = train.pop('성별')

from sklearn.model_selection import train_test_split
X_tr, X_val, y_tr, y_val = train_test_split(train, target, test_size = 0.2, random_state = 2022)

from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(random_state=2022)
model.fit(X_tr, y_tr)
pred = model.predict_proba(X_val)

from sklearn.metrics import roc_auc_score
print(roc_auc_score(y_val, pred[:,1]))

pred = model.predict_proba(test)
submit = pd.DataFrame({
	'pred': pred[:,1]
})
submit.to_csv('00000.csv', index=False)

0

프로세스가 시작되었습니다.(입력값을 직접 입력해 주세요)
> 0.6415146489773355
Makefile:6: recipe for target 'py3_run' failed
make: *** [py3_run] Error 1
Traceback (most recent call last):
  File "/goorm/Main.out", line 33, in <module>
    pred = model.predict_proba(test)
  File "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py", line 674, in predict_proba
    X = self._validate_X_predict(X)
  File "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py", line 422, in _validate_X_predict
    return self.estimators_[0]._validate_X_predict(X, check_input=True)
  File "/usr/local/lib/python3.9/dist-packages/sklearn/tree/_classes.py", line 407, in _validate_X_predict
    X = self._validate_data(X, dtype=DTYPE, accept_sparse="csr",
  File "/usr/local/lib/python3.9/dist-packages/sklearn/base.py", line 437, in _validate_data
    self._check_n_features(X, reset=reset)
  File "/usr/local/lib/python3.9/dist-packages/sklearn/base.py", line 365, in _check_n_features
    raise ValueError(
ValueError: X has 73 features, but DecisionTreeClassifier is expecting 74 features as input.

프로세스가 종료되었습니다.

1

컬럼의 수가 다르네요

인코딩 후에 달라졌습니다.

레이블인코딩 또는 합쳐서 인코딩하면됩니다

인코딩 추가 영상 (커리큘럼 하단)을 참고해주세요

0

아하 감사합니다!!