Author(s): AbhinayaPinreddy

Originally published on Towards AI.

The Mistake I Made

Picture this: You’ve built a stock prediction model using 50 technical indicators. It looks perfect on your training data. You’re about to invest real money based on its predictions. Then you notice something weird — yesterday, the model said “buy strongly,” but today, with almost identical market conditions, it screams “sell immediately.”

This article dives into the common pitfalls of machine learning models, particularly linear regression. It explains how traditional models can fail with real-world data and introduces Ridge and Lasso regression as solutions. The author emphasizes the importance of regularization techniques, which help maintain model stability by penalizing extreme weight values, ultimately leading to more reliable predictions. The trade-offs between bias and variance are discussed, demonstrating why simpler, more interpretable models often outperform complex, overfitted ones in practical applications.

Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI