
前提
ここに質問の内容を詳しく書いてください。
(例)
pythonでデータ予測を行っています。
モデルで予測model.fit(X_train,y_train)の時に、以下のエラーメッセージが発生しました。
実現したいこと
予測ができるようにする
発生している問題・エラーメッセージ
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) Input In [118], in <cell line: 30>() 28 # 回帰モデルの呼び出し 29 model = LinearRegression() ---> 30 model.fit(X_train,y_train) File ~\anaconda3\lib\site-packages\sklearn\linear_model\_base.py:662, in LinearRegression.fit(self, X, y, sample_weight) 658 n_jobs_ = self.n_jobs 660 accept_sparse = False if self.positive else ["csr", "csc", "coo"] --> 662 X, y = self._validate_data( 663 X, y, accept_sparse=accept_sparse, y_numeric=True, multi_output=True 664 ) 666 if sample_weight is not None: 667 sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype) File ~\anaconda3\lib\site-packages\sklearn\base.py:581, in BaseEstimator._validate_data(self, X, y, reset, validate_separately, **check_params) 579 y = check_array(y, **check_y_params) 580 else: --> 581 X, y = check_X_y(X, y, **check_params) 582 out = X, y 584 if not no_val_X and check_params.get("ensure_2d", True): File ~\anaconda3\lib\site-packages\sklearn\utils\validation.py:964, in check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator) 961 if y is None: 962 raise ValueError("y cannot be None") --> 964 X = check_array( 965 X, 966 accept_sparse=accept_sparse, 967 accept_large_sparse=accept_large_sparse, 968 dtype=dtype, 969 order=order, 970 copy=copy, 971 force_all_finite=force_all_finite, 972 ensure_2d=ensure_2d, 973 allow_nd=allow_nd, 974 ensure_min_samples=ensure_min_samples, 975 ensure_min_features=ensure_min_features, 976 estimator=estimator, 977 ) 979 y = _check_y(y, multi_output=multi_output, y_numeric=y_numeric) 981 check_consistent_length(X, y) File ~\anaconda3\lib\site-packages\sklearn\utils\validation.py:800, in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator) 794 raise ValueError( 795 "Found array with dim %d. %s expected <= 2." 796 % (array.ndim, estimator_name) 797 ) 799 if force_all_finite: --> 800 _assert_all_finite(array, allow_nan=force_all_finite == "allow-nan") 802 if ensure_min_samples > 0: 803 n_samples = _num_samples(array) File ~\anaconda3\lib\site-packages\sklearn\utils\validation.py:114, in _assert_all_finite(X, allow_nan, msg_dtype) 107 if ( 108 allow_nan 109 and np.isinf(X).any() 110 or not allow_nan 111 and not np.isfinite(X).all() 112 ): 113 type_err = "infinity" if allow_nan else "NaN, infinity" --> 114 raise ValueError( 115 msg_err.format( 116 type_err, msg_dtype if msg_dtype is not None else X.dtype 117 ) 118 ) 119 # for object dtype data, we only check for NaNs (GH-13254) 120 elif X.dtype == np.dtype("object") and not allow_nan: ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
該当のソースコード
import os
os.chdir(r'C:\フォルダー名')
import pandas as pd
import numpy as npfrom sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
CSVファイルの読み込み
df = pd.read_csv(r'C:\フォルダー名/ファイル名.csv')
#df = StandardScaler().fit_transform(df)
説明変数
X=df.iloc[1:,2:11]
目的変数
y=df.iloc[1:,11]
#訓練データ,テストデータに分ける
X_train, X_test, y_train, y_test =train_test_split(X,y,random_state=0)
回帰モデルの呼び出し
model = LinearRegression()
model.fit(X_train,y_train)
補足情報(FW/ツールのバージョンなど)
Python 3.9.13
