Machine Learning (ML) is like teaching computers to learn from data. It helps machines make predictions or decisions without being explicitly programmed. If you’re new to ML, some of the terms might sound confusing. Let’s break down the key terminologies in simple words.
Algorithm
An algorithm in machine learning is like a recipe. It’s a set of steps or instructions that a computer follows to solve a problem. For example, a decision tree algorithm helps the machine decide by asking yes/no questions.
Model
A model is the final product of machine learning. Think of it as the machine’s brain, trained to understand patterns in the data. For example, if you train a model to recognize cats in pictures, it uses what it learned to predict if a new picture has a cat or not.
Training Data
Training data is the information given to the machine for learning. For example, if you want a model to identify fruits, you give it pictures of apples, bananas, and oranges along with their names. The machine uses this data to understand how fruits look.
Testing Data
Testing data checks how well the model has learned. It’s like a quiz for the machine after training. You give it new data (not seen during training) and see if it predicts correctly.
Features
Features are the inputs or characteristics used to make predictions. For example, in predicting the price of a house, features could include the number of bedrooms, location, and size of the house.
Labels
Labels are the correct answers in supervised learning. For example, if you’re training a model to recognize animals, the labels could be “cat,” “dog,” or “bird” for each picture.
Supervised Learning
In supervised learning, the machine learns from labeled data. You tell it what’s right and wrong. For example, teaching it that a picture of an apple is labeled as “apple.”
Unsupervised Learning
Here, the machine gets data without labels. It tries to find patterns on its own. For example, grouping customers into similar categories based on their shopping habits without knowing their preferences beforehand.
Underfitting
Underfitting occurs when a model doesn’t learn enough from the training data. It’s too simple to make accurate predictions and performs poorly on both training and testing data.
Accuracy
Accuracy is the percentage of correct predictions made by the model. For example, if the model predicts 8 out of 10 correctly, the accuracy is 80%.
Precision and Recall
- Precision: Focuses on how many of the predicted results were actually correct.
- Recall: Focuses on how many actual correct results were identified by the model.
These are important when dealing with imbalanced datasets (e.g., detecting rare diseases).
