Splitting your dataset is a fundamental practice in building reliable AI and machine learning models. Without a proper data split, your model might overfit, underperform, or give misleading results.

What are Data Splits and Why are They Important?

What Are the Data Splits?

SplitPurpose
Training SetIt is used to teach the model patterns in the data
Validation SetUsed for AI model fine-tune services, model hyperparameters and avoid overfitting
Test SetHelps evaluate the final model’s performance on unseen data

Why It Matters

  • Prevents Overfitting – Helps ensure that your model generalizes well to new data.
  • Improves Accuracy – Fine-tunes models to perform better on future predictions.
  • Enables Fair Evaluation – Test set gives an unbiased performance estimate.

How to Split the Data?

Recommended Ratios (can vary based on dataset size):

  • Training: 60–70%
  • Validation: 15–20%
  • Test: 15–20%

General Steps to Split Data:

  1. Load and preprocess your dataset.
  2. Split into training and temp (test + validation).
  3. Split the temp further into validation and test.

Code Example – Train/Validation/Test Split

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
# Sample dataset
data = {
'Age': [22, 25, 47, 52, 46, 56, 48, 33, 27, 29],
'Salary': [20000, 25000, 47000, 52000, 46000, 56000, 48000, 33000, 27000, 29000],
'Purchased': [0, 0, 1, 1, 1, 1, 1, 0, 0, 0]
}
df = pd.DataFrame(data)
# Features and target
X = df[['Age', 'Salary']]
y = df['Purchased']
# Step 1: Train-Test split (80% train_val, 20% test)
X_train_val, X_test, y_train_val, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Step 2: Train-Validation split (from the 80%)
X_train, X_val, y_train, y_val = train_test_split(X_train_val, y_train_val, test_size=0.25, random_state=42)
# (Now: 60% train, 20% val, 20% test)
# Step 3: Train model on training set
model = LogisticRegression()
model.fit(X_train, y_train)
# Step 4: Tune using validation set (example: simple accuracy check)
val_predictions = model.predict(X_val)
print("Validation Accuracy:", accuracy_score(y_val, val_predictions))
# Step 5: Final evaluation on test set
test_predictions = model.predict(X_test)
print("Test Accuracy:", accuracy_score(y_test, test_predictions))

Sample Output:

Validation Accuracy: 1.0

Test Accuracy: 1.0

Conclusion

Divide the data into proper training, validation and test sets to ensure trustworthy and professional AI software development services. Doing so: 

  • Helps detect and prevent overfitting.
  • Improves model tuning and performance.
  • Provides a real-world performance estimate.

Always treat your test set as “untouched” data. That’s how you’ll know your model works when it truly matters — in production.