Introduction to Scikit-learn
Chapter 1: Introduction
Scikit-learn
As mentioned, scikit-learn is a machine learning library, with many different models/algorithms already implemented for you.
Scikit-learn focusses on modelling data. It leaves the loading, handling, manipulation and data visualisation to be handled by the other libraries.
Here are some examples of what scikit-learn provides:
- Datasets
- Supervised models
- Feature selection
- Clustering of unlabelled data
- Dataset transformation (preprocessing, feature extraction, normalization, dimensionality reduction, etc.)
- Model selection and evaluation (cross validation, hyperparameter tuning, classification metrics, etc.)
- Ensemble methods (Boosting, Bagging, Random Forest, etc)
You can use all the provided ML models as a blackbox, without really having to understand what goes on inside (remember the word abstraction?) Having said that, it is always good to at least have a high-level intuition of what each model is or what an algorithm does!
Scikit-learn is quite easy to use. Once you understand the basic use and syntax of one model, you can easily switch to another model since they all have a similar interface.
Scikit-learn is also pretty much object-oriented, so hope you have all your OOP knowledge ready and raring to go!