Машинное обучение. Отличие задач прогнозирования от задач регрессии

x0        x1        x2        x3        x4        x5        x6         y
0.284297  0.292773  0.290652  0.045383  0.564894  0.347683  0.014610  0.961696
0.939509  0.993534  0.772005  0.164555  0.800897  0.591883  0.190720  0.040162
0.281475  0.641496  0.695424  0.993351  0.958840  0.457999  0.203841  0.007968
0.569055  0.622889  0.762683  0.095271  0.101407  0.510155  0.542256  0.848998
0.106809  0.068440  0.075260  0.435980  0.412090  0.226181  0.909518  0.714608
0.751670  0.902665  0.353395  0.975563  0.823437  0.742916  0.760047  0.567249

x1 0.284297  0.292773  0.290652  0.045383  0.564894  0.347683  0.014610  0.961696
x2 0.939509  0.993534  0.772005  0.164555  0.800897  0.591883  0.190720  0.040162
x3 0.281475  0.641496  0.695424  0.993351  0.958840  0.457999  0.203841  0.007968
x4 0.569055  0.622889  0.762683  0.095271  0.101407  0.510155  0.542256  0.848998
x5 0.106809  0.068440  0.075260  0.435980  0.412090  0.226181  0.909518  0.714608
x6 0.751670  0.902665  0.353395  0.975563  0.823437  0.742916  0.760047  0.567249

import pickle
import xgboost as xgb
import pandas as pd
import numpy as np
import math
 
from sklearn.metrics import confusion_matrix, mean_squared_error
from sklearn.datasets import load_iris, load_digits, load_boston
from sklearn.metrics import mean_absolute_error,mean_squared_error,median_absolute_error, accuracy_score
df = pd.read_csv('file1.csv',";",header=None)
X_train = df.drop(8,axis=1)
Y_train = df[8]
test_data = pd.read_csv('file2.csv',";",header=None)
X_test = test_data.drop(8,axis=1)
Y_test = test_data[8]
xgb_model = xgb.XGBRegressor(max_depth)
cl = xgb_model.fit(X_train,Y_train)
predictions = cl.predict(X_test)
actuals = Y_test
print(mean_absolute_error(actuals, predictions))
print(mean_squared_error(actuals, predictions))
print(median_absolute_error(actuals, predictions))