Table of contents
Instance-Based Machine Learning 🗂️
🔹 Description:
- Instance-based learning, also known as lazy learning, stores the training data and makes predictions by comparing new data points with the stored instances. No explicit model is created during the training phase.
🔹 Example:
- k-Nearest Neighbors (k-NN) 🧑🤝🧑: When predicting a new data point's label, k-NN identifies the 'k' closest points (instances) in the training dataset and bases its prediction on the majority label among them.
🔹 Advantages:
👍 Simplicity: Easy to understand and implement.
⏱️ No Training Time: Instant "training" as no model is built.
🔄 Adaptability: Easily adapts to new data without needing to update a model.
🔹 Disadvantages:
🐢 Slow Prediction: Can be slow, especially with large datasets.
💾 Memory Intensive: Requires storing the entire dataset.
⚠️ Sensitive to Noise: Outliers can significantly affect predictions.
Model-Based Machine Learning 🛠️
🔹 Description:
- Model-based learning, also known as eager learning, involves building a model based on the training data. This model is then used to make predictions on new data. The model is created and "learns" during the training phase.
🔹 Example:
- Linear Regression 📈: A model is built by finding the best-fit line that predicts the relationship between input features and the target variable.
🔹 Advantages:
⚡ Fast Prediction: Predictions are usually faster as they don't require comparison with every instance in the dataset.
💽 Efficient Storage: Only the model parameters need to be stored, not the entire dataset.
🛡️ Less Sensitive to Noise: Properly tuned models can handle outliers and noise better.
🔹 Disadvantages:
⏳ Training Time: Building the model can be time-consuming.
🧠 Complexity: Some models require significant expertise to tune and optimize.
📉 Less Flexible: The model might not adapt well to new data unless retrained.
🔚 Conclusion:
Both instance-based and model-based machine learning methods have their pros and cons depending on the problem at hand. Instance-based learning is straightforward and adaptive but can be slow and resource-intensive, while model-based learning is efficient in prediction and storage but requires a more complex setup. Understanding these differences helps in choosing the right approach for your machine learning tasks.