In its vanilla form logistic regression is used to do binary classification. Multiclass classification with logistic regression can be done either through the one-vs-rest scheme in which for each class a binary classification problem of data belonging or not to that class is done, or changing the loss function to cross- entropy loss.
Decision trees are constructed via an algorithmic approach that identifies ways to split a data set based on different conditions. It is one of the most widely used and practical methods for supervised learning. Decision Trees are a non-parametric supervised learning method used for both classification and regression tasks.
Open task 1 notebook in colab:
Open task 2 notebook in colab:
How to get the data using wget?
# Obsesity Dataset for Task 1
!wget https://github.com/DeepConnectAI/challenge-week-4/raw/master/data/obesity_data.csvThis is not a huge repository therefore can easily be downloaded.
# Clone the repository
$ git clone https://github.com/DeepConnectAI/challenge-week-4.git
# Go to project directory
$ cd challenge-week-4
# Open up the jupyter notebook if installed
$ jupyter notebook