2、通过train_test_split拆分训练集和测试集并评估模型性能 #从xgboost中导入XGBClassifierfromxgboostimportXGBClassifierfromxgboostimportplot_importance#导入train_test_split用于拆分数据集fromsklearn.model_selectionimporttrain_test_split#导入accuracy_score用于评估模型的准确率fromsklearn.metricsimportaccuracy_scoreimport...
See the below code to get a reproducible way to cause this issue. fromsklearn.datasetsimportmake_classificationfromxgboostimportXGBClassifierimportxgboostasxgb# Large synthetic datasetX, y = make_classification(n_samples=500_0000, n_features=20, n_informative=10, n_redundant=10, random_state=42...
For this kind of problem, I created shap-hypetune: a python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models In your case, this enables you to perform RFE with XGBClassifier in a very simple and intuitive way: from shaphypetune import BoostRFE m...
from xgboost import XGBClassifier from sklearn.preprocessing import LabelEncoder import time # load data data = read_csv('train.csv') dataset = data.values # split data into X and y X = dataset[:,0:94] y = dataset[:,94] # encode string class values as integers label_encoded_y = Labe...
from xgboost import XGBClassifier model = XGBClassifier.fit(X,y) # importance_type = ['weight', 'gain', 'cover', 'total_gain', 'total_cover'] model.get_booster().get_score(importance_type='weight') However, the method below also returns feature importance's and that have differen...
from xgboost import XGBClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score # load data dataset = loadtxt('pima-indians-diabetes.csv', delimiter=",") # split data into X and y X = dataset[:,0:8] Y = dataset[:,8] # split data int...
import pandas as pd import xgboost as xgb from sklearn import preprocessing from sklearn.model_selection import train_test_split from sklearn.externals import joblib from xgboost import XGBClassifier from sklearn.metrics import accuracy_score from xgboost import plot_importance import sys def load_feat...
As you can see in the code below, the API is very similar to XGBoost. The highlighted portions are where the code is different than the normal XGBoost API. from xgboost_ray import RayXGBClassifier, RayParams from sklearn.datasets import load_breast_cancer ...
However, I don't know how to save the best model once the model with the best parameters has been discovered. How do I go about doing so? Here is my code: import xgboost as xgb from sklearn.model_selection import StratifiedKFold, GridSearchCV xgb_model = xgb.XGBClassifier(objective = ...
model = xgboost.XGBClassifier(objective="multi:softmax") model.fit(X_train, y_train)defget_ABS_SHAP(df_shap,df):#import matplotlib as plt# Make a copy of the input datashap_v = pd.DataFrame(df_shap) feature_list = df.columns