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Condense Random Forest into a Decision Tree
Preserve generalization power while reducing run-time.
 • 
Avi Chawla
3
Transform Decision Tree into Matrix Operations.
Make classical ML models deployment friendly.
 • 
Avi Chawla
2
Skorch: The Power of PyTorch Combined with The Elegance of Sklearn
The best of both worlds.
 • 
Avi Chawla
The Categorization of Clustering Algorithms in Machine Learning
6 types of clustering algorithms in a single frame.
 • 
Avi Chawla
Avoid Using PCA for Visualization Unless the CEV Plot Says So
Using variance as an indicator for visualization.
 • 
Avi Chawla
4
The Motivation Behind Using KernelPCA over PCA for Dimensionality Reduction
...and when to not use KernelPCA.
 • 
Avi Chawla
3
Why Sklearn’s Logistic Regression Has no Learning Rate Hyperparameter?
What are we missing here?
 • 
Avi Chawla
1
Decision Trees ALWAYS Overfit! Here's a Neat Technique to Prevent It
Balancing cost and model size.
 • 
Avi Chawla
Why Decision Trees Must Be Thoroughly Inspected After Training
Understanding the structural formulation of decision trees and how it can be problematic at times.
 • 
Avi Chawla
Two Simple Yet Immensely Powerful Techniques to Supercharge kNN Models
Addressing some common limitations of traditional kNNs.
 • 
Avi Chawla
2
An Overlooked Source of (Massive) Run-time Optimization in KMeans
Another issue with KMeans and here's how to address it.
 • 
Avi Chawla
How Does a Mini-Batch Implementation of KMeans Clustering Work?
A step-by-step visual guide to mini-batch KMeans.
 • 
Avi Chawla
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