Why mastering classical classifiers matters before jumping into Deep Learning
Open LinkedIn and you’ll see buzz everywhere — Transformers, LLMs, and Generative AI. It’s easy to feel left behind if you’re not fine-tuning massive models. But here’s the truth: complex problems don’t always need complex solutions. (Medium)
Complex problems don’t always need complex solutions.
Think of it this way: you don’t use a flamethrower to light a candle.
Before you try to master the “magic” of Deep Learning, you need to master the reliability of the classics. (Medium)
In this post, we’ll break down three essential supervised learning algorithms that form the foundation of machine learning. They are fast, effective, and — unlike deep neural networks — interpretable. (Medium)
1. The Copycat: K-Nearest Neighbors (KNN)
The Visual Intuition
Imagine you move into a new neighborhood but don’t know if it’s a “party” or “quiet” area. So you look at your three closest neighbors:
- Neighbor 1: Throws parties every weekend.
- Neighbor 2: Throws parties every weekend.
- Neighbor 3: Reads quietly in the garden.
Since most neighbors are partying, you assume you’re in a party neighborhood. That’s the essence of K-Nearest Neighbors — you classify a new point based on the “vote” of the K closest labeled points. (Medium)
Code Implementation (Python)
# 1. Import the model
from sklearn.neighbors import KNeighborsClassifier
# 2. Instantiate the model (choosing K=3 neighbors)
knn_model = KNeighborsClassifier(n_neighbors=3)
# 3. Fit the model to your data
knn_model.fit(X_train, y_train)
# 4. Make predictions on new data
predictions = knn_model.predict(X_new_data)
Using scikit-learn makes this model quick and simple to train and use. (Medium)
2. The Probability Calculator: Logistic Regression
The Visual Intuition
KNN tells you what class something is — but sometimes you want how confident the model is. Logistic Regression works like a dimmer switch rather than a binary light switch. (Medium)
Instead of a straight yes/no boundary, it fits an S-shaped curve (Sigmoid) to your data. Predictions are probabilities:
-
0.99→ very likely positive -
0.51→ positive but with low confidence
This nuance is crucial in business contexts — e.g., deciding whether to send an email or make a call based on churn probability. (Medium)
Code Implementation (Python)
from sklearn.linear_model import LogisticRegression
log_reg = LogisticRegression()
log_reg.fit(X_train, y_train)
predictions = log_reg.predict(X_new_data)
probabilities = log_reg.predict_proba(X_new_data)
Logistic Regression gives you both class predictions and associated probabilities. (Medium)
3. The Boundary Builder: Support Vector Machine (SVM)
The Visual Intuition
If Logistic Regression is about probabilities, SVM is about boundaries. Imagine trying to separate red balls from blue balls on a table with a stick. You might place the stick anywhere — but SVM chooses the position that gives the widest possible margin between classes. (Medium)
A wider margin generally helps the model generalize better to new, unseen data. (Medium)
Code Implementation (Python)
from sklearn.svm import SVC # "Support Vector Classifier"
svm_model = SVC(kernel='linear')
svm_model.fit(X_train, y_train)
predictions = svm_model.predict(X_new_data)
Switch kernels (e.g., RBF, polynomial) to capture nonlinear relationships. (Medium)
Conclusion: Your New Toolkit
You now have three powerful tools in your machine learning arsenal:
- KNN: intuitive baseline
- Logistic Regression: probability-aware predictions
- SVM: clear, robust decision boundaries
Most importantly, these aren’t magic — they are geometry and code. Next step: code them yourself on a simple dataset like Iris. (Medium)
Follow-Up
Follow me for Part 2, where we will break the rules of geometry with Decision Trees and Neural Networks. (Medium)
Top comments (1)
The point about "complex problems don’t always need complex solutions" really resonates. In my experience, I often found that while diving into deep learning and neural networks, I overlooked the effectiveness of simpler classifiers like logistic regression and decision trees. They can provide surprisingly good results for many problems and often require less tuning and computational power. It's a humbling reminder that sometimes the simplest approach can be the most effective.