Model Training and Workflow

1 min read

Imagine teaching a child how to recognize cats đŸ±. You show them 100 photos—some of cats, some not—and say “This is a cat”, “This is not a cat.” Over time, they learn to distinguish cats on their own.

That’s exactly what model training is:

“Training” 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.

đŸ§Ș 2. Training vs Testing: What’s the Difference? #

A very common confusion—so let’s clear this up.

đŸ·ïž Category🧠 Training SetđŸ§Ș Testing Set
PurposeTeach the modelEvaluate the model
Seen by ModelYesNo
Use CaseLearning from dataChecking how well it learned
AnalogyStudy material for an examActual 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.

🔄 This separation helps ensure the model can generalize well and not just memorize answers.

Step-by-Step ML Training Workflow #

Let’s 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:

  • 70–80% for training
  • 20–30% 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 “final exam.” No cheating allowed!

Step 7: Evaluate & Improve #

Measure accuracy, tweak parameters, re-train, or change models.

Training in Real Life: Real-World Use Case #

Let’s say you’re 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: #

  1. You gather 5 years of job offer data.
  2. Clean and preprocess the dataset.
  3. Train the model using Logistic Regression.
  4. Test using 20% of data.
  5. 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. đŸ”„

Summary Table #

ConceptDescription
Model TrainingFeeding examples to let the model learn patterns
Model TestingEvaluating the model’s ability to perform on unseen data
FeaturesInputs used to make predictions
LabelsActual answers the model should predict
Updated on June 5, 2025