Intermediate Machine Learning with scikit-learn

Cross validation, Parameter Tuning, Pandas Interoperability, and Missing Values

By Thomas J. Fan

Link to slides

Scikit-learn is a Python machine learning library used by data science practitioners from many disciplines. We will learn about cross-validation, tuning machine learning algorithms, and pandas interoperability during this training. Cross-validation enables us to evaluate our machine learning models by splitting our data into multiple training and testing datasets. We will learn to handle missing values with imputation using univariate and multivariate techniques. Next, we will explore tuning algorithms in scikit-learn with grid search and random search. We will learn about categorical features and how to use scikit-learn's encoders to convert these categorical features into numerical features for a machine-learning algorithm to consume. Finally, we will apply the machine learning techniques on a house pricing dataset with scikit-learn's Histogram-based Gradient Boosted Trees. scikit-learn's boosted tree implementation is based on LightGBM and has similar performance characteristics.

Obtaining the Material

With git

The most convenient way to download the material is with git:

git clone https://github.com/thomasjpfan/ml-workshop-intermediate-1-of-2

Please note that I may add and improve the material until shortly before the session. You can update your copy by running:

git pull origin master

Download zip

If you are not familiar with git, you can download this repository as a zip file at: github.com/thomasjpfan/ml-workshop-intermediate-1-of-2/archive/master.zip. Please note that I may add and improve the material until shortly before the session. To update your copy please re-download the material a day before the session.

Running the notebooks

Local Installation

Local installation requires conda to be installed on your machine. The simplest way to install conda is to install miniconda by using an installer for your operating system provided at docs.conda.io/en/latest/miniconda.html. After conda is installed, navigate to this repository on your local machine:

cd ml-workshop-intermediate-1-of-2

Then download and install the dependencies:

conda env create -f environment.yml

This will create a virtual environment named ml-workshop-intermediate-1-of-2. To activate this environment:

conda activate ml-workshop-intermediate-1-of-2

Finally, to start jupyterlab run:

jupyter lab

This should open a browser window with the jupterlab interface.

Run with Google's Colab

If you have any issues with installing conda or running jupyter on your local computer, then you can run the notebooks on Google's Colab:

0. Quick Review of scikit-learn
1. Cross-Validation in scikit-learn
2. Parameter tuning
3. Missing values in scikit-learn
4. Pandas Interoperability

License

This repo is under the MIT License.