𝘿𝙖𝙩𝙖 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝙉𝙖𝙩𝙞𝙤𝙣📊 on Instagram: "📍ENSEMBLE LEARNING – PART 10.2: BAGGING (BOOTSTRAP AGGREGATION) Bagging — short for Bootstrap Aggregation — is one of the simplest yet most powerful ensemble techniques in machine learning. Here’s how it works step-by-step 👇 1️⃣ Bootstrap Sampling: We randomly select multiple subsets of the training data with replacement. That means some samples might repeat, and some might be left out — giving each model a slightly different perspective of
0:10
Instagramdatascience.nation
𝘿𝙖𝙩𝙖 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝙉𝙖𝙩𝙞𝙤𝙣📊 on Instagram: "📍ENSEMBLE LEARNING – PART 10.2: BAGGING (BOOTSTRAP AGGREGATION) Bagging — short for Bootstrap Aggregation — is one of the simplest yet most
In this video, we explore the concept of Bagging, also known as Bootstrap Aggregation, and its significance in ensemble learning. By combining the outputs of multiple models, Bagging reduces overfitting and improves generalizability. We also discuss its relationship with Random Forests and its applications in data science.
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