Imagine an Excel sheet with 10,000 rows and 10 columns, representing data like stock prices, house prices, medical records, or anything else. As a human, analysing such a massive dataset to uncover meaningful patterns or make predictions would be incredibly challenging.
This is where machine learning comes to the rescue. It enables computers to process vast amounts of data, uncover patterns, make predictions—such as forecasting stock or house prices—or even evaluate the accuracy of medical procedures, all with remarkable efficiency.
Machine learning is the subset of artificial intelligence, which enables computers to learn from the data without being explicitly programmed.
Building Blocks of a Machine Learning Model
Data
Data is the foundation on which your machine learning model is built. The quality and quantity of data directly influence the model's performance. The phrase "Garbage in, Garbage out" holds true—if the data is flawed or irrelevant, the model's predictions will be unreliable. Ensuring clean, accurate, and representative data is critical.
Features
Features are the measurable characteristics or attributes extracted from your data that the model uses for learning.
Example: For stock prices with OHLC (Open, High, Low, Close) data, features like moving averages or RSI (Relative Strength Index) can be created.
For other domains:
Medical data: Features could include age, blood pressure, or cholesterol levels.
Real estate: Features might include house size, location, and number of rooms.
Effective feature engineering helps the model uncover patterns more easily.
Algorithm
An algorithm is the set of instructions the model follows to interpret the data and learn from it. It defines the way the model processes inputs to produce outputs.
Examples of algorithms:
- Decision Trees
- Linear Regression
- Naive Bayes
- The choice of algorithm depends on the type of problem (classification, regression, clustering, etc.) and the data structure.
Training and Testing Data
The data is typically split into two parts:
- Training Data: Used to train the model, allowing it to learn patterns and relationships within the data.
- Testing Data: Used to evaluate how well the model performs on unseen data, ensuring it generalizes effectively.
- A common split is 80:20 or 70:30, but this can vary based on the dataset size and problem requirements.
Model
The model is the output of the machine learning process—a representation of the patterns and relationships learned from the data. It is used to make predictions or decisions.
- After creating the model, additional steps like hyperparameter tuning, cross-validation, or benchmarking can further enhance its performance and reliability.
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