Imagine teaching a child how to recognize cats đ±. You show them 100 photosâsome of cats, some notâand say “This is a cat”, “This is not a cat.” Over time, they learn to distinguish cats on their own.
Thatâs exactly what model training is:
âTrainingâ an algorithm using data so it can make predictions or decisions without being explicitly programmed.
In technical terms:
- You feed the algorithm examples (data).
- It learns patterns.
- It becomes a trained model that can make predictions on new data.
đ§Ș 2. Training vs Testing: Whatâs the Difference? #
A very common confusionâso letâs clear this up.
đ·ïž Category | đ§ Training Set | đ§Ș Testing Set |
---|---|---|
Purpose | Teach the model | Evaluate the model |
Seen by Model | Yes | No |
Use Case | Learning from data | Checking how well it learned |
Analogy | Study material for an exam | Actual exam paper |
- Training Data: You feed this data to the model.
- Testing Data: You hide this data from the model during training and only use it to see how well it performs.
đ This separation helps ensure the model can generalize well and not just memorize answers.
Step-by-Step ML Training Workflow #
Letâs walk through what happens behind the scenes when you train a model.
Step 1: Gather Data #
Collect structured or unstructured data from real-world sources like databases, sensors, user logs, etc.
Example: You get 10,000 rows of past house sales data.
Step 2: Preprocess Data #
Clean the data: fill missing values, normalize, encode categories, etc.
Think of this like prepping ingredients before cooking.
Step 3: Split the Data #
Divide into:
- 70â80% for training
- 20â30% for testing
Sometimes there’s a third split called the validation set.
Step 4: Choose an Algorithm #
Pick an algorithm based on the problem:
- Regression: Linear Regression
- Classification: Decision Trees, SVM, Logistic Regression
- Clustering: K-Means
Step 5: Train the Model #
Feed training data into the model so it can learn.
It’s like a student reading textbooks and doing exercises.
Step 6: Test the Model #
Use the test data to evaluate how well the model performs on unseen data.
This is the âfinal exam.â No cheating allowed!
Step 7: Evaluate & Improve #
Measure accuracy, tweak parameters, re-train, or change models.
Training in Real Life: Real-World Use Case #
Letâs say youâre working in HR Tech, and you want to build a model that predicts whether a job applicant will accept a job offer.
a. Features: #
- Salary offered
- Years of experience
- Distance from office
- Industry
- Education
b. Label: #
- Offer accepted: Yes or No
c. Process: #
- You gather 5 years of job offer data.
- Clean and preprocess the dataset.
- Train the model using Logistic Regression.
- Test using 20% of data.
- The model gives you 85% accuracy on unseen data.
Now recruiters can use this prediction to improve offer acceptance rates by tweaking offers or timing. đ„
Summary Table #
Concept | Description |
---|---|
Model Training | Feeding examples to let the model learn patterns |
Model Testing | Evaluating the modelâs ability to perform on unseen data |
Features | Inputs used to make predictions |
Labels | Actual answers the model should predict |