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SMOTE is an oversampling technique where the synthetic samples are generated for the minority class. Handle imbalanced data using SMOTE.
In this article we will learn about common feature selection filter based techniques to increase the efficiency of your model.
After training our model and have predicted the outcomes, we need to evaluate the model's performance. And here comes our Confusion Matrix.
Understand the problem of overfitting in decision trees and learn to solve it by minimal cost-complexity pruning using Scikit-Learn in Python.
Learn how to use data augmentation, resampling techniques, & cost-sensitive learning for solving class imbalance in machine learning.
Build image classification models in Pytorch and TensorFlow. Learn CNN for image classification on MNIST dataset and analyze the performance of the model.
Compare Random Forest and Decision Tree algorithms through detailed explanations, Python examples, and insights on model performance.
scikit-learn, or sklearn, is a powerful machine learning library in Python. Master these sklearn tips, tricks and hacks to become a better data scientist.
How can you deploy a machine learning model into production? That's where we use Flask, an awesome tool for model deployment in machine learning.
A beginners guide for machine learning with C++. In this article learn about linear and logistic regression and how to implement them using C++.
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