Questions to Ask Before Creating a Machine Learning Model | by Karim Samir | simplifann | Mar, 2025

Earlier than constructing a machine studying or deep studying mannequin, you should ask your self key inquiries to outline the issue, select the best strategy, and guarantee profitable coaching. Right here’s a structured information that will help you:

1️⃣ What’s the downside I’m fixing?

• Instance: Am I predicting one thing? Classifying one thing? Translating textual content?

2️⃣ What kind of output do I would like?

• 🔹 Classification (e.g., Is that this electronic mail spam or not?)
• 🔹 Regression (e.g., Predicting home costs)
• 🔹 Clustering (e.g., Grouping clients by purchasing habits)
• 🔹 Sequence Prediction (e.g., Textual content translation, speech recognition)

3️⃣ What’s the enterprise or real-world influence of fixing this?

• Instance: Will it save time, enhance effectivity, or enhance accuracy?

4️⃣ Do I’ve labeled or unlabeled information?

• Labeled information → Supervised Studying
• Unlabeled information → Unsupervised Studying

5️⃣ What’s the construction of my information?

• 🔹 Tabular Information (Spreadsheets, Databases)
• 🔹 Picture Information (Pictures, Medical Scans)
• 🔹 Textual content Information (Emails, Evaluations, Chat Conversations)
• 🔹 Time-Sequence Information (Inventory Costs, Sensor Readings)

6️⃣ How a lot information do I’ve?

• 🔹 Sufficient information? → Prepare a deep mannequin
• 🔹 Restricted information? → Use pre-trained fashions or information augmentation

7️⃣ Is my information clear and balanced?

• Are there lacking values?
• Are courses imbalanced (e.g., 90% constructive, 10% detrimental)?
• Do I would like information augmentation (for pictures/textual content)?

8️⃣ What kind of mannequin fits my downside?

• 🔹 Neural Community (ANN, CNN, RNN, Transformer)?
• 🔹 Choice Tree, Random Forest, or XGBoost?
• 🔹 SVM or Logistic Regression?

9️⃣ What structure ought to I take advantage of?

• What number of layers?
• What number of neurons per layer?
• Which activation capabilities (ReLU, Sigmoid, Softmax, and so on.)?

🔹 Instance:

• For picture classification → CNN
• For text-based duties → LSTMs, Transformers
• For structured information → XGBoost, MLP

🔟 What loss operate ought to I take advantage of?

• 🔹 For classification? → binary_crossentropy or categorical_crossentropy
• 🔹 For regression? → mean_squared_error (MSE), mean_absolute_error (MAE)

1️⃣1️⃣ What optimizer ought to I select?

• 🔹 Adam (Good default)
• 🔹 SGD (For giant datasets)
• 🔹 RMSprop (For recurrent networks)

1️⃣2️⃣ How do I forestall overfitting?

• 🔹 Dropout layers
• 🔹 L2 regularization
• 🔹 Extra coaching information

1️⃣3️⃣ How do I consider efficiency?

• 🔹 Accuracy, Precision, Recall (for classification)
• 🔹 RMSE, R² (for regression)

1️⃣4️⃣ How will I deploy my mannequin?

• API (Flask, FastAPI)
• Cell App (TensorFlow Lite)
• Internet App (Streamlit)

1️⃣5️⃣ How will I deal with new information?

• Will the mannequin be retrained periodically?
• Will it want real-time predictions?