5 ML Models I Wish I Respected Earlier.

Read time: 2.5 minutes

5 models that earned my respect the hard way:

1. Linear and Logistic Regression
   I assumed they were too basic to matter. What I learned is that they expose flawed data and weak assumptions faster than anything else. They force feature discipline and produce explanations that leaders understand. When these models fail, the issue usually lies in the thinking, not the math.

2. Decision Trees
   I thought they belonged in classrooms. In practice, trees reveal how data actually drives decisions. Their logic stays visible, interactions surface naturally, and scaling stays simple. They overfit easily and shift often, which is why they excel at understanding rather than winning benchmarks.

3. Random Forest
   I treated it as a quick baseline. Over time, it became the model I reach for when feature trust runs low. It handles noise well and delivers stable performance. The tradeoff is interpretability. Random Forest buys time and reliability, not deep insight.

4. Gradient Boosting
   I believed better tuning solved everything. Boosting taught me otherwise. It magnifies strong features and punishes sloppy ones. Performance rises fast, but mistakes rise faster. These models reward discipline and expose shortcuts without mercy.

5. Neural Networks
   I once viewed them as the endgame. Experience reframed that view. They shine on unstructured data and scale well with volume and compute. On tabular data, they often add complexity without value. When trees outperform a neural network there, the issue is not hardware.

💡Key Takeaway: 

Models do not create leverage on their own, but judgment does. Senior data scientists stop chasing complexity for status and start using it with intent. Respect is earned through restraint.