46. Deep Learning vs Classical ML: When to Use Which
46.3 Side-by-Side Comparison
Why. A side-by-side table settles arguments in class.
What. Compare data type, feature work, compute, explainability, and typical first tool.
How.
Read the comparison
- Tabular + explainable → sklearn.
- Perception (vision/speech) → deep nets.
- Always keep a dumb baseline.
rows = [
("data", "tidy columns", "images/text/raw signals"),
("features", "you engineer", "layers learn features"),
("compute", "laptop CPU fine", "GPU helps sooner"),
("explain", "coef_/trees easier", "harder (research ongoing)"),
("start with", "sklearn", "PyTorch"),
]
for name, classical, deep in rows:
print(f"{name:8} | {classical:20} | {deep}")
Ravindra Bagale's Tip
Baseline nehmi. Deep net haravla tar shame nahi — data/problem mismatch asel.