if we see this simple data
1. Data Preparation
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2. Model Training and Evaluation
- Linear Support Vector Classifier (LinearSVC)
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- Model accuracy on the test set: 76.95% (
- Random Forest Classifier
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- Model accuracy on the test set: 83.54% (
### Observations:
- Random Forest performs better than LinearSVC on this dataset.
1. Data Preparation
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disease.drop("target", axis=1): Extracts feature variables (X). -
disease["target"]: Extracts the target variable (y). -
train_test_split(x, y, train_size=0.2): Splits the data into training and test sets, with 20% allocated for training.2. Model Training and Evaluation
- Linear Support Vector Classifier (LinearSVC)
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LinearSVC() is initialized and trained using fit(x_train, y_train). - Model accuracy on the test set: 76.95% (
0.7695).- Random Forest Classifier
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RandomForestClassifier(n_estimators=100): A Random Forest model with 100 decision trees. - Model accuracy on the test set: 83.54% (
0.8354), which is better than LinearSVC.### Observations:
- Random Forest performs better than LinearSVC on this dataset.
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📌 Model Comparison & Evaluation in Machine Learning
When building classification models, evaluating them properly ensures the best performance. Here’s how to do it effectively:
🔹 Key Evaluation Metrics
✅ Accuracy – Measures overall correctness but isn’t ideal for imbalanced datasets.
✅ AUC-ROC – Higher AUC means better class separation.
✅ Confusion Matrix – Shows the breakdown of correct & incorrect predictions.
✅ Classification Report – Includes Precision, Recall, and F1-score for deeper insights.
🔹 Comparing Multiple Models
1️⃣ Train different models (Logistic Regression, SVM, Random Forest, etc.).
2️⃣ Use Cross-Validation to get reliable performance scores.
3️⃣ Optimize with Hyperparameter Tuning (GridSearchCV, RandomizedSearchCV).
4️⃣ Compare models using AUC-ROC, F1-score, or accuracy for better decision-making.
#MachineLearning #AI #ModelEvaluation #DataScience #AUCROC #ConfusionMatrix 🚀
When building classification models, evaluating them properly ensures the best performance. Here’s how to do it effectively:
🔹 Key Evaluation Metrics
✅ Accuracy – Measures overall correctness but isn’t ideal for imbalanced datasets.
✅ AUC-ROC – Higher AUC means better class separation.
✅ Confusion Matrix – Shows the breakdown of correct & incorrect predictions.
✅ Classification Report – Includes Precision, Recall, and F1-score for deeper insights.
🔹 Comparing Multiple Models
1️⃣ Train different models (Logistic Regression, SVM, Random Forest, etc.).
2️⃣ Use Cross-Validation to get reliable performance scores.
3️⃣ Optimize with Hyperparameter Tuning (GridSearchCV, RandomizedSearchCV).
4️⃣ Compare models using AUC-ROC, F1-score, or accuracy for better decision-making.
#MachineLearning #AI #ModelEvaluation #DataScience #AUCROC #ConfusionMatrix 🚀
Key Takeaway: No single metric or model is best—always compare multiple models and use multiple evaluation metrics for better insights!
Forwarded from Tech Nerd (Tech Nerd)
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Breaking Down the Values
1. Accuracy
Value: 85.25% (printed as a percentage)
Meaning: The model correctly classified 85.25% of the test samples.
2. Precision
Value: 0.8484 (≈ 84.84%)
Meaning: Out of all the positive predictions the model made, 84.84% were actually correct.
Interpretation: If the model predicts "disease" (positive class), it is correct 84.84% of the time.
3. Recall
Value: 0.875 (≈ 87.5%)
Meaning: Out of all the actual positive cases, the model correctly identified 87.5% of them.
Interpretation: If someone actually has the disease, the model detects it 87.5% of the time.
4. F1-Score
Value: 0.8615 (≈ 86.15%)
Meaning: A balance between precision and recall, combining both into one score.
Interpretation: The model maintains an 86.15% trade-off between capturing all positive cases (recall) and making correct positive predictions (precision).
1. Accuracy
Value: 85.25% (printed as a percentage)
Meaning: The model correctly classified 85.25% of the test samples.
2. Precision
Value: 0.8484 (≈ 84.84%)
Meaning: Out of all the positive predictions the model made, 84.84% were actually correct.
Interpretation: If the model predicts "disease" (positive class), it is correct 84.84% of the time.
3. Recall
Value: 0.875 (≈ 87.5%)
Meaning: Out of all the actual positive cases, the model correctly identified 87.5% of them.
Interpretation: If someone actually has the disease, the model detects it 87.5% of the time.
4. F1-Score
Value: 0.8615 (≈ 86.15%)
Meaning: A balance between precision and recall, combining both into one score.
Interpretation: The model maintains an 86.15% trade-off between capturing all positive cases (recall) and making correct positive predictions (precision).
ML Scoring Parameter – Choose Wisely! 🧠🤖
Trained a model? Great! But… how do you know if it’s actually GOOD? 🤔
Well
🔹 For Classification (Spam Detector, Disease Prediction, etc.):
✅ Accuracy: % of correct predictions (but useless if classes are imbalanced )
🎯 Precision: Out of all "positives" predicted, how many were actually right?
📢 Recall: Did we catch all actual positives? (Good for detecting fraud, diseases, etc.)
⚖️ F1 Score: Balances precision & recall (best when you need both!)
🔹 For Regression (House Prices, Stock Prediction, etc.):
📊 R² Score: How well does the model explain the variance in data?
📏 MAE (Mean Absolute Error): How much do we miss by, on average?
💥 MSE (Mean Squared Error): Bigger errors hurt more! (Good for penalizing bad predictions)
💡 Choose the right metric, or risk using a bad model! Don't just rely on accuracy—understand what your data actually needs.
Follow for more ML & AI insights! #MachineLearning #AI #DataScience
Trained a model? Great! But… how do you know if it’s actually GOOD? 🤔
Well
🔹 For Classification (Spam Detector, Disease Prediction, etc.):
✅ Accuracy: % of correct predictions (but useless if classes are imbalanced )
🎯 Precision: Out of all "positives" predicted, how many were actually right?
📢 Recall: Did we catch all actual positives? (Good for detecting fraud, diseases, etc.)
⚖️ F1 Score: Balances precision & recall (best when you need both!)
🔹 For Regression (House Prices, Stock Prediction, etc.):
📊 R² Score: How well does the model explain the variance in data?
📏 MAE (Mean Absolute Error): How much do we miss by, on average?
💥 MSE (Mean Squared Error): Bigger errors hurt more! (Good for penalizing bad predictions)
💡 Choose the right metric, or risk using a bad model! Don't just rely on accuracy—understand what your data actually needs.
Follow for more ML & AI insights! #MachineLearning #AI #DataScience
🔥6
1. R² Score (Coefficient of Determination)
Value: 0.8674 (≈ 86.74%)
Meaning: The model explains 86.74% of the variance in the target variable.
Interpretation: A higher R² (closer to 1) means better performance. If R² is 0, the model performs no better than predicting the mean.
2. Mean Absolute Error (MAE)
Value: 2.0595
Meaning: On average, the model's predictions differ by about 2.06 units from the actual values.
Interpretation: Lower MAE means better accuracy. It's easier to interpret than MSE but doesn’t penalize large errors as much.
3. Mean Squared Error (MSE)
Value: 9.7192
Meaning: The average squared difference between predicted and actual values is 9.72 units².
Interpretation: Larger errors are heavily penalized due to squaring. A lower MSE is better, but it's harder to interpret compared to MAE
Value: 0.8674 (≈ 86.74%)
Meaning: The model explains 86.74% of the variance in the target variable.
Interpretation: A higher R² (closer to 1) means better performance. If R² is 0, the model performs no better than predicting the mean.
2. Mean Absolute Error (MAE)
Value: 2.0595
Meaning: On average, the model's predictions differ by about 2.06 units from the actual values.
Interpretation: Lower MAE means better accuracy. It's easier to interpret than MSE but doesn’t penalize large errors as much.
3. Mean Squared Error (MSE)
Value: 9.7192
Meaning: The average squared difference between predicted and actual values is 9.72 units².
Interpretation: Larger errors are heavily penalized due to squaring. A lower MSE is better, but it's harder to interpret compared to MAE
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One minute, I’m tuning hyperparameters… next thing I know, my brain is overfitting! 😂 But for real, ML is WILD—turning data into intelligence feels like magic🙂
🤣5
How to Improve a Predictive Model?
1. Evaluate Performance
Compare with a baseline model (simple predictor).
Check key metrics (accuracy, RMSE, etc.).
2. Improve Data
Collect more data if possible.
Clean, preprocess, and engineer better features.
3. Choose a Better Model
Try different algorithms (e.g., Decision Trees, SVM, Neural Networks) we'll see them in future
4. Tune Hyperparameters
Adjust settings like learning rate, tree depth, or batch size.
•Use GridSearchCV or •RandomizedSearchCV for optimization.
5. Prevent Overfitting & Underfitting
•Overfitting? Regularization, dropout, or pruning.
•Underfitting? More features, deeper models.
Optimize these steps, and your model will improve!
1. Evaluate Performance
Compare with a baseline model (simple predictor).
Check key metrics (accuracy, RMSE, etc.).
2. Improve Data
Collect more data if possible.
Clean, preprocess, and engineer better features.
3. Choose a Better Model
Try different algorithms (e.g., Decision Trees, SVM, Neural Networks) we'll see them in future
4. Tune Hyperparameters
Adjust settings like learning rate, tree depth, or batch size.
•Use GridSearchCV or •RandomizedSearchCV for optimization.
5. Prevent Overfitting & Underfitting
•Overfitting? Regularization, dropout, or pruning.
•Underfitting? More features, deeper models.
Optimize these steps, and your model will improve!
Hyperparameters are settings on a model you can adjust to improve its ability to find patterns
Parameters are patterns that the model finds in the data
Parameters are patterns that the model finds in the data
Improving hyperparameters by hand means manually adjusting hyperparameter values through trial and error instead of using automated tuning methods like GridSearchCV or RandomizedSearchCV.
How It Works:
*Start with default hyperparameters.
*Train the model and evaluate performance.
*Adjust one hyperparameter at a time (e.g. max features nestimators, max depth).
*Retrain and compare results.
*Repeat until you find the best settings.
How It Works:
*Start with default hyperparameters.
*Train the model and evaluate performance.
*Adjust one hyperparameter at a time (e.g. max features nestimators, max depth).
*Retrain and compare results.
*Repeat until you find the best settings.
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RandomizedSearchCV (Faster Alternative)
🎯 How it works:
Randomly selects a subset of hyperparameter combinations instead of trying all.
Still uses cross-validation to evaluate performance.
Saves time by focusing on random but diverse samples.
✅ Pros:
✔️ Much faster than GridSearchCV.
✔️ Works well when there are many hyperparameters.
❌ Cons:
❌ Might not find the absolute best combination (since it’s random).
❌ Less exhaustive compared to GridSearchCV.
🎯 How it works:
Randomly selects a subset of hyperparameter combinations instead of trying all.
Still uses cross-validation to evaluate performance.
Saves time by focusing on random but diverse samples.
✅ Pros:
✔️ Much faster than GridSearchCV.
✔️ Works well when there are many hyperparameters.
❌ Cons:
❌ Might not find the absolute best combination (since it’s random).
❌ Less exhaustive compared to GridSearchCV.