{"id":146018,"date":"2025-07-21T11:14:02","date_gmt":"2025-07-21T05:44:02","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=146018"},"modified":"2025-07-21T11:14:02","modified_gmt":"2025-07-21T05:44:02","slug":"introduction-to-xgboost-algorithm","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/introduction-to-xgboost-algorithm\/","title":{"rendered":"Introduction to XGBoost Algorithm"},"content":{"rendered":"<h3>Program 1<\/h3>\n<p><a href=\"https:\/\/drive.google.com\/file\/d\/1PDqQrUmLuJW1Sd95vS-XTFYhvjkmbEKw\/view?usp=sharing\" target=\"_blank\" rel=\"noopener\"><strong>Diabetes Prediction Dataset<\/strong><\/a><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">import pandas as pd\r\nfrom sklearn.model_selection import train_test_split\r\nfrom xgboost import XGBClassifier\r\nfrom sklearn.metrics import accuracy_score\r\nfrom sklearn.preprocessing import LabelEncoder\r\n\r\n# Load data\r\ndf = pd.read_csv(\"D:\/\/scikit_data\/diabetes\/diabetes_prediction_dataset.csv\")  # columns: Glucose, BMI, etc.\r\nX = df.drop(\"Outcome\", axis=1) # input\r\ny = df[\"Outcome\"] #output\r\n\r\n# Train-test split\r\n\r\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\r\n\r\n# Use_label_encoder=False\r\n# What it means: Prevents XGBoost from using its old internal label encoder (which caused warnings in newer versions).\r\n# Why you need it: In older versions of XGBoost, categorical labels were encoded internally. Now, it's better to use pandas\/scikit-learn encoders externally.\r\n# Set to False to avoid a warning like:\r\n\r\n#eval_metric='logloss'\r\n#This sets the evaluation metric to be used during training.\r\n#logloss (logarithmic loss) is a common metric for binary classification:\r\n#It penalizes wrong confident predictions more than slightly wrong ones.\r\n\r\nmodel = XGBClassifier(use_label_encoder=False, eval_metric='logloss')\r\nmodel.fit(X_train, y_train)\r\n\r\n# Predict\r\ny_pred = model.predict(X_test)\r\n# Accuracy\r\nprint(\"Accuracy:\", accuracy_score(y_test, y_pred))\r\n<\/pre>\n<p><span hidden class=\"__iawmlf-post-loop-links\" data-iawmlf-links=\"[{&quot;id&quot;:16,&quot;href&quot;:&quot;https:\\\/\\\/drive.google.com\\\/file\\\/d\\\/1PDqQrUmLuJW1Sd95vS-XTFYhvjkmbEKw\\\/view?usp=sharing&quot;,&quot;archived_href&quot;:&quot;&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[],&quot;broken&quot;:false,&quot;last_checked&quot;:null,&quot;process&quot;:&quot;done&quot;}]\"><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Program 1 Diabetes Prediction Dataset import pandas as pd from sklearn.model_selection import train_test_split from xgboost import XGBClassifier from sklearn.metrics import accuracy_score from sklearn.preprocessing import LabelEncoder # Load data df = pd.read_csv(&#8220;D:\/\/scikit_data\/diabetes\/diabetes_prediction_dataset.csv&#8221;) # columns: Glucose,&#46;&#46;&#46;<\/p>\n","protected":false},"author":581,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[36],"tags":[34931,8431,33127,33128,34932,34933,16483],"class_list":["post-146018","post","type-post","status-publish","format-standard","hentry","category-machine-learning","tag-introduction-to-xgboost-algorithm","tag-machine-learning","tag-machine-learning-practical","tag-machine-learning-program","tag-what-is-xgboost-algorithm","tag-xgboost-algorithm","tag-xgboost-algorithm-introduction"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - 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