Python 決定木 エラー

実現したいこと

はやたすという方のYouTube動画に出てくるモデル(Kaggleの、タイタニックのデータで、生存したかどうかを判別するもの)と同じ物の作成、原因解明、モデルでの予測を目標にしています。

発生している問題・分からないこと

sklearnでmodelを作成後、model.fit()でエラーが発生しています。

エラーメッセージ

error

1幾つか出ています 2line 30, in <module> 3 model.fit(X_train, y_train) 4line 1152, in wrapper 5 return fit_method(estimator, *args, **kwargs) 6line 959, in fit 7 super()._fit( 8line 242, in _fit 9 X, y = self._validate_data( 10line 617, in _validate_data 11 X = check_array(X, input_name="X", **check_X_params) 12line 938, in check_array 13 raise ValueError( 14ValueError: Expected 2D array, got 1D array instead: 15array=[0. 1. 1. 1. 0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 1. 16 0. 1. 0. 0. 1. 0. 0. 1. 1. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 1. 1. 0. 0. 1. 17 0. 0. 0. 0. 1. 1. 0. 1. 1. 0. 1. 0. 0. 1. 0. 0. 0. 1. 1. 0. 1. 0. 0. 0. 18 0. 0. 1. 0. 0. 0. 1. 1. 0. 1. 1. 0. 1. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 19 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 20 0. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 1. 1. 0. 21 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 1. 1. 0. 22 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 1. 1. 0. 0. 1. 0. 23 1. 1. 1. 1. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 1. 1. 1. 0. 1. 0. 0. 0. 1. 24 1. 0. 1. 0. 1. 0. 0. 0. 1. 0. 1. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 1. 0. 0. 25 0. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 0. 1. 0. 0. 26 0. 0. 0. 1. 1. 1. 0. 1. 1. 0. 1. 1. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 1. 0. 27 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 0. 1. 0. 1. 1. 1. 0. 1. 1. 1. 28 0. 0. 0. 1. 1. 0. 1. 1. 0. 0. 1. 1. 0. 1. 0. 1. 1. 1. 1. 0. 0. 0. 1. 0. 29 0. 1. 1. 0. 1. 1. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 1. 30 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 0. 0. 0. 0. 1. 1. 0. 0. 0. 1. 1. 0. 1. 31 0. 0. 0. 1. 0. 1. 1. 1. 0. 1. 1. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 1. 32 0. 0. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 1. 1. 1. 33 1. 0. 0. 1. 0. 1. 0. 0. 1. 0. 0. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 1. 0. 1. 34 0. 1. 1. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 35 0. 0. 0. 1. 1. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 36 1. 0. 1. 1. 0. 1. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 0. 1. 0. 0. 1. 0. 37 0. 0. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 0. 1. 1. 0. 1. 1. 0. 38 0. 1. 1. 0. 1. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 0. 0. 39 1. 1. 0. 1. 1. 1. 0. 0. 0. 1. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 1. 40 1. 0. 0. 0. 1. 0. 0. 1. 1. 1. 0. 0. 1. 0. 0. 1. 0. 0. 1. 0. 0. 1. 1. 0. 41 0. 0. 0. 1. 0. 0. 1. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 1. 1. 1. 0. 1. 42 0. 1. 0. 1. 0. 1. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 1. 1. 0. 43 0. 1. 0. 0. 0. 1. 0. 1. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 44 0. 1. 0. 0. 1. 1. 0. 0. 0. 0. 1. 1. 1. 1. 1. 0. 1. 0. 0. 0. 1. 1. 0. 0. 45 1. 0. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 1. 0. 46 1. 0. 0. 1. 0. 0. 1. 1. 0. 0. 1. 1. 0. 0. 0. 1. 0. 0. 1. 1. 0. 1. 0. 0. 47 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 1. 1. 1. 0. 0. 0. 0. 1. 0. 1. 0. 0. 0. 48 0. 0. 0. 0. 1. 1. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 49 0. 0. 0. 0. 1. 1. 0. 1. 0. 0. 0. 1. 1. 1. 1. 1. 0. 0. 0. 1. 0. 0. 1. 1. 50 0. 0. 1. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 1. 1. 1. 1. 0. 0. 0. 1. 0. 51 0. 1. 1. 0. 0. 1. 0. 1. 0. 0. 1. 1. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 1. 52 0. 1. 0.]. 53Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

該当のソースコード

Python

1import numpy as np 2import pandas as pd 3from sklearn.tree import DecisionTreeClassifier 4from sklearn.model_selection import train_test_split 5 6train_df = pd.read_csv("train.csv")7test_df = pd.read_csv("test.csv")8 9df = pd.concat([train_df, test_df], ignore_index = True)10 11df2 = df.drop(columns=["Cabin", "Fare", "Ticket", "SibSp", "Parch", "Name"])12df3 = df2.copy()13df3["Embarked"] = df3["Embarked"].fillna("S")14df_age_median = df["Age"].median()15df3["Age"] = df3["Age"].fillna(df_age_median)16 17ohe_embarked = pd.get_dummies(df3["Embarked"], dtype=int)18df5 = pd.concat([df3, ohe_embarked], axis = 1)19df5 = df5.drop(columns = "Embarked")20df5["Sex"] = pd.get_dummies(df5["Sex"], drop_first = True, dtype=int)21 22train = df5[~df5["Survived"].isnull()]23test = df5[df5["Survived"].isnull()]24test = test.drop(columns = ["Survived"])25 26X_train = train["Survived"]27y_train = train.drop(columns=["PassengerId", "Survived"])28 29model = DecisionTreeClassifier(random_state=42)30model.fit(X_train, y_train)

試したこと・調べたこと

上記の詳細・結果

model.fit()の引数をnumpyで行列にしましたが、エラーでした。

補足

特になし