Supervised Learning is a type of 🤖 ML where an algorithm is trained on labeled data, consisting of input-output pairs, to predict the output for unseen inputs.
Notes
Info
It’s like teaching by example.
Summary
Supervised Learning is analogous to ateacher providing examples to astudent and then testing their ability to apply the learned knowledge
- For instance, in email spam detection, labeled examples of spam and non-spam emails are used to train a model
- The model learns to classify new emails as spam or not, improving the user experience
graph TD; A[Training Data]:::main --> B[Model]:::main; B --> C[Predictions]:::main; A -----> D[Expected Output]; C <-.->|Goal is to minimise this gap|D; classDef main stroke:#eee
The essence of supervised learning is teaching machines to learn fromlabel -ed data to make predictions or decisions
TakeAways
- 📌 Supervised Learning requires labeled datasets to train models capable of making predictions on new, unseen data.
- Examples include 🏷 Classification and 🔄 Regression tasks
- Models use input-output mappings to minimize prediction errors
- 💡 Algorithms such as Linear Regression, support vector machines, and Neural Network are common in supervised learning
- ⚡ Model performance is evaluated using metrics like Accuracy, Precision, Recall, and mean squared error.
Process
- 🗄️ Data Collect: Collect and label a dataset
- 📁 Preprocess the Data: Clean, normalize, and split the data into training and testing sets
- 🏋️ Train the model: Use a supervised learning algorithm to learn patterns from the training data
- 📊 Evaluate the model: Test on unseen data and measure performance with metrics.
Thoughts
- 🤖 Great for Predictors: Best suited for tasks where historical labeled data is available
- 🏷️ Labeling is crucial: Performance heavily relies on the quality and size of the dataset
- 🔄 Iterative Process: Requires repeated training and tuning to optimize results
- 🚢 Deploy and monitor: Deploy the model and refine it based on real-world performance