This is just a demo repository in which I am trying out on different functions of Python libraries used in Machine Learning.
MACHINE LEARNING:
- Supervised Learning:
- Regression
- Classification
- Unsupervised Learning:
- Clustering
| SUPERVISED MACHINE LEARNING | |
|---|---|
| Regression | Classification |
| Linear Regression | Logistic Regression |
| Regularized Regression | Linear discriminant analysis |
| K-nearest neighbors (KNN) | K-nearest neighbors (KNN) |
| Decision tree regressort (CART) | Decision tree classifier (CART) |
| Support vector regression | Support vector classifier |
| AdaBoost | AdaBoost |
| Gradient boosting method | Gradient boosting method |
| Random forest method | Random forest method |
| Extra trees | Extra trees |
- When values are continuous we use Regression and when values are categorical we use Classification.
- Linear Regression is always used in Regression and Logistic Regression is always used in Classification problems
- KNN algorithm can be used in both Regression and Classification. It uses the concept of Euclidean distance.
- CART algorithm can be used in both Regression and Classification. It uses the concept of finding entropy.
- Different algorithms give different accuracies, we need to select the algo which gives maximum accuracy.