What is learning_rate and epochs in machine learning

In Machine Learning, learning_rate and epochs are two crucial hyperparameters:

1. Learning Rate (learning_rate):

Definition:
The learning rate controls how quickly or slowly your model adjusts its internal parameters (weights) based on errors from training data.

• High learning rate (e.g., 0.1):

  • Learns faster, but risks "overshooting" the best solution.
  • Can result in unstable or inaccurate training.

• Low learning rate (e.g., 0.0001):

  • Learns slower but more accurately.
  • Takes longer but typically produces more stable results.

Typical Values: Usually between 0.1 (high) and 0.00001 (low).

2. Epochs (epochs):

Definition:
An epoch is one complete pass through the entire training dataset.

• One epoch:

  • Your model has "seen" every training example exactly once.

• Multiple epochs:

  • Your model repeatedly learns from the data multiple times.
  • More epochs mean more chances for your model to learn patterns, potentially improving accuracy, but may cause overfitting if too high.

Typical Values: Usually range from 10 to hundreds or thousands, depending on the dataset size and complexity.

Example (Simple Scenario):

• Learning rate: 0.01
• Epochs: 100

Means your model will update slowly and carefully, making 100 passes through your entire dataset.

In short:

• Learning rate controls how quickly your model learns.
• Epochs define how many times your model sees the entire training dataset.

Finding the right balance helps your model perform at its best.