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🙂
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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.
GridSearchCV (Exhaustive Search)
🔍 How it works:
Tries every possible combination of hyperparameters from a predefined set.
Uses cross-validation to evaluate each combination.
Selects the best performing set.
✅ Pros:
✔️ Finds the best hyperparameters since it checks all options.
✔️ Ensures optimal tuning when the search space is small.
❌ Cons:
❌ Very slow if there are many parameters and values.
❌ Computationally expensive.
🔍 How it works:
Tries every possible combination of hyperparameters from a predefined set.
Uses cross-validation to evaluate each combination.
Selects the best performing set.
✅ Pros:
✔️ Finds the best hyperparameters since it checks all options.
✔️ Ensures optimal tuning when the search space is small.
❌ Cons:
❌ Very slow if there are many parameters and values.
❌ Computationally expensive.
Mike's ML Forge
Ofc im in class room😁
I already filled and cleaned the missing values also seeing some visualisation mannn this so fun😅
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Feature Encoding 101: Prepare Data For Machine Learning
various feature encoding methods. These are important in order to turn all sorts of features into meaningful numerical representations.