Articles by Dr. Darrin

Random Forest Classification with Python

March 31, 2019 | Dr. Darrin

Random forest is a type of machine learning algorithm in which the algorithm makes multiple decision trees that may use different features and subsample to making as many trees as you specify. The trees then vote to determine the class of an example. This approach helps to deal with the ...
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Data Exploration Case Study: Credit Default

February 21, 2019 | Dr. Darrin

Exploratory data analysis is the main task of a Data Scientist with as much as 60% of their time being devoted to this task. As such, the majority of their time is spent on something that is rather boring compared to building models. This post will provide a simple example of ...
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RANSAC Regression in Python

February 7, 2019 | Dr. Darrin

RANSAC is an acronym for Random Sample Consensus. What this algorithm does is fit a regression model on a subset of data that the algorithm judges as inliers while removing outliers. This naturally improves the fit of the model due to the removal of some data points. The process that ...
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Combining Algorithms for Classification with Python

January 20, 2019 | Dr. Darrin

Many approaches in machine learning involve making many models that combine their strength and weaknesses to make more accuracy classification. Generally, when this is done it is the same algorithm being used. For example, random forest is simply many decision trees being developed. Even when bagging or boosting is being ...
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Gradient Boosting Regression in Python

January 13, 2019 | Dr. Darrin

In this  post, we will take a look at gradient boosting for regression. Gradient boosting simply makes sequential models that try to explain any examples that had not been explained by previously models. This approach makes gradient boosting superior to AdaBoost. Regression trees are mostly commonly teamed with boosting. There ...
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Gradient Boosting Classification in Python

January 8, 2019 | Dr. Darrin

Gradient Boosting is an alternative form of boosting to AdaBoost. Many consider gradient boosting to be a better performer than adaboost. Some differences between the two algorithms is that gradient boosting uses optimization for weight the estimators. Like adaboost, gradient boosting can be used for most algorithms but is commonly ...
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AdaBoost Regression with Python

January 6, 2019 | Dr. Darrin

This post will share how to use the adaBoost algorithm for regression in Python. What boosting does is that it makes multiple models in a sequential manner. Each newer model tries to successful predict what older models struggled with. For regression, the average of the models are used for the ...
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AdaBoost Classification in Python

January 1, 2019 | Dr. Darrin

Boosting is a technique in machine learning in which multiple models are developed sequentially. Each new model tries to successful predict what prior models were unable to do. The average for regression and majority vote for classification are used. For classification, boosting is commonly associated with decision trees. However, boosting ...
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Recommendation Engine with Python

December 25, 2018 | Dr. Darrin

Recommendation engines make future suggestion to a person based on their prior behavior. There are several ways to develop recommendation engines but for purposes, we will be looking at the development of a user-based collaborative filter. This type of filter takes the ratings of others to suggest future items to ...
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Elastic Net Regression in Python

December 23, 2018 | Dr. Darrin

Elastic net regression combines the power of ridge and lasso regression into one algorithm. What this means is that with elastic net the algorithm can remove weak variables altogether as with lasso or to reduce them to close to zero as with ridge. All of these algorithms are examples of ...
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