XGBoostError: проверка не удалась: m == 1 || п == 1: ⇐ Python

[Anonymous]

Я получаю следующую ошибку. Пожалуйста, помогите. y_train выглядит как массив размером 1 d.
XGBoostError: [09:23:19] C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i- 0015a694724fa8361-1\xgboost\xgboost-ci-windows\src\data\array_interface.h:218: Проверка не удалась: m == 1 || n == 1:
import pandas as pd
url="https://raw.githubusercontent.com/shavi ... onSep1.csv"
df=pd.read_csv(url)
df.head()
import numpy as np
df["Yr2"]=np.where(df["Yr2"]=="X",0, df["Yr2"])
df["Yr2"]=np.where(df["Yr2"]=="Y",0, df["Yr2"])
df["Yr2"].fillna(1,inplace=True)
df["Yr2"].astype("str")
import seaborn as sns
sns.histplot(df["HS GPA"])
md=df["HS GPA"].median()
df["HS GPA"].fillna(md,inplace=True)
df["Yr2"]=df["Yr2"].astype("str")
df["AcdStand1stSem"].fillna("0",inplace=True)
df["SO"].fillna("N",inplace=True)
df["First Gener"].fillna("N",inplace=True)
df["EnterTransfHrs"].fillna(0.0,axis=0,inplace=True)
df["Earn Hrs < 30"].value_counts()
df["Earn Hrs < 30"].fillna("1.0",inplace=True)
df.dropna(subset=["Majr at Adm "], inplace=True)
df["FA"].fillna("Unknown",inplace=True)
df["RegTermCred for Yr1 Fall"].fillna("15.28",inplace=True)
df["Gender"].fillna("M",inplace=True)
df["ResdCode"].fillna("R",inplace=True)

df["Major"].fillna("Unknown",inplace=True)
mn=df["GPA1stSem"].mean()
df["GPA1stSem"].fillna(mn,inplace=True)
df["Admit Type"].fillna("FR",inplace=True)
df["Summer PAL"].fillna("N",inplace=True)
df["PR State"].fillna("MA",inplace=True)
df["IPEDS Race/Ethnicity"].fillna("Unknown",inplace=True)
df["ResHall"].fillna("Unknown",inplace=True)
df.dropna(subset=['HS GPA Group'], inplace=True)
df.dropna(subset=['GPA 1st Term Group'], inplace=True)
df['Spring Census Return'].fillna(0, inplace=True)
df["Sports sum"]=df["Sports sum"].astype("str")
df["Spring Census Return"]=df["Spring Census Return"].astype("str")
df=pd.get_dummies(df,drop_first=True)
df["HS GPA"]=(df["HS GPA"]-df["HS GPA"].min())/(df["HS GPA"].max()-df["HS GPA"].min())
df["GPA1stSem"]=(df["GPA1stSem"]-df["GPA1stSem"].min())/(df["GPA1stSem"].max()-df["GPA1stSem"].min())
df["EnterTransfHrs"]=(df["EnterTransfHrs"]-df["EnterTransfHrs"].min())/(df["EnterTransfHrs"].max()-df["EnterTransfHrs"].min())
df["AttHrs Yr1"]=(df["AttHrs Yr1"]-df["AttHrs Yr1"].min())/(df["AttHrs Yr1"].max()-df["AttHrs Yr1"].min())
df["RegTermCred for Yr1 Fall"]=(df["RegTermCred for Yr1 Fall"]-df["RegTermCred for Yr1 Fall"].min())/(df["RegTermCred for Yr1 Fall"].max()-df["RegTermCred for Yr1 Fall"].min())
df["RegTermCred for Yr1 Spring"]=(df["RegTermCred for Yr1 Spring"]-df["RegTermCred for Yr1 Spring"].min())/(df["RegTermCred for Yr1 Spring"].max()-df["RegTermCred for Yr1 Spring"].min())
before= df.iloc[:,:6]
after= df.iloc[:, 7:]
target= df.iloc[:,6]
df=pd.concat([before,after,target], axis=1,join="outer")
X=df.iloc[:,:-1]
df["Yr2_1"]=df["Yr2_1"].astype("int")
y=df.iloc[:,-1]
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=42)
from sklearn.metrics import classification_report
!pip install xgboost
from xgboost import XGBClassifier
#from sklearn.preprocessing import LabelEncoder
#encoder=LabelEncoder()
#y_train=encoder.fit_transform(y_train)
model=XGBClassifier(objective="binary:logistic",n_estimators=100,max_depth=3, learning_rate=.1)
model.fit(X_train,y_train)
y_pred=model.predict(X_test)


Подробнее здесь: https://stackoverflow.com/questions/791 ... ed-m-1-n-1