A.I, Data and Software Engineering

Search results forregression

Decision Tree Regression quick note

Petamind A.I

Decision tree builds regression or classification models in the form of a tree structure. It breaks down a dataset into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. The final result is a tree with decision nodes and leaf nodes. A decision node (e.g., Outlook) has two or more branches (e.g., Sunny, Overcast and Rainy), each representing...

Metrics for regression and classification – quick note

In this article, we review some common metrics and their uses for two main ML problems, i.e. regression and classification. Regression Metrics Most of the blogs have focussed on classification metrics like precision, recall, AUC etc. For a change, I wanted to explore all kinds of metrics including those used in regression as well. MAE and RMSE are the two most popular metrics for continuous...


git rebase

Rebasing is the process of moving or combining a sequence of commits to a new base commit. Rebasing is most useful and easily visualized in the context of a feature branching workflow. The visualized general process is as the following: From a content perspective, rebasing is changing the base of your branch from one commit to another making it appear as if you’d created your branch from a...

Predict coronavirus deaths by days

As the pandemic is going on with an increasing number of deaths daily, let create a simple model to predict the deaths caused by 2019-nCoV (Wuhan Coronavirus). The 2019-nCoV death data I grab the death toll data from World Meters website. DateDaily DeathsFeb. 889Feb. 786……Jan. 2416Jan. 238 Plot the data Firstly, we transform the table into a Pandas data frame. 123 death_toll =...

Common Loss functions and their uses – quick note


Machines learn by means of a loss function which reflects how well a specific model performs with the given data. If predictions deviate too much from actual results, loss function would yield a very large value. Gradually, with \(optimization\) function, parameters are modified accordingly to reduce the error in prediction. In this article, we will quickly review some common loss functions and...

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