In the previous parts, we have covered types of ML, how models make predictions, and how to measure their success. Now, let’s unlock the final level: tuning and training smarter models.
In this post, we will explore:
- Hyperparameter Tuning
- Gradient Descent
- Loss Functions
- Epochs
These are the dials, levers, and engines behind every great machine learning model.
Hyperparameter Tuning
Hyperparameters are configuration settings (not learned from data) that define how your model trains. For example, number of trees in a random forest, learning rate in neural networks.
Tuning:
We try multiple combinations to find the best-performing model.
Sample Python Code Snippet:
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier
params = {'n_estimators': [50, 100], 'max_depth': [5, 10]}
grid = GridSearchCV(RandomForestClassifier(), params, cv=3)
grid.fit(X, y)
print(grid.best_params_)
Real-world use case:
Tuning a recommendation engine’s parameters to improve click-through rates.
Gradient Descent
It is the algorithm that powers learning in most ML models.
Gradient Descent adjusts model parameters (like weights in linear regression) by minimizing the error step by step.
To explain it better, it is like walking down a hill in the fog. Each step (gradient) gets you closer to the bottom (minimum loss).
Concept:
# Concept: w = w - learning_rate * gradient
Real-world use case:
Used in neural networks, credit risk models, even pricing engines.
Loss Function
A function that measures how wrong the model is. The goal of training is to minimize this loss.
Examples:
- Regression: Mean Squared Error (MSE)
- Classification: Binary Cross-Entropy
Sample Python Code Snippet:
from sklearn.metrics import mean_squared_error
mse = mean_squared_error(y_true, y_pred)
print("MSE:", mse)
Real-world use case:
Optimizing loan default prediction to reduce risk exposure.
Epoch
An epoch is one complete pass through the entire training dataset.
More epochs = better learning
Too many = overfitting
Concept:
model.fit(X_train, y_train, epochs=10)
Real-world use case:
Used in training image recognition models (e.g., identifying vehicle types in traffic cams).
How do these work together?
- Hyperparameters = your recipe (how many eggs, oven temp).
- Gradient Descent = you adjusting as you bake.
- Loss Function = taste test to see how off it is.
- Epochs = how many times you try baking to get it just right.
That’s a wrap!
Hope by this time you would have understood what Machine Learning is, exploring how predictions are made, measuring if models are good and finally, tuning them to be a great.