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To penalise or not to penalise: The curious case of automatic feature selection

What is Lasso Regression? The LASSO (Least Absolute Shrinkage and Selection Operator)  is a shrinkage and selection method for linear regression. This method involves penalizing the absolute size of the regression coefficients. A good description for layman understanding is given on this SO post; to quote, ” By penalizing (or equivalently constraining the sum of the absolute…

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A random forest approach to predicting breast cancer in working class women

What is a Random Forest? A random forest is an ensemble (group or combination) of tree’s that collectively vote for the most popular class (or feature) amongst them by cancelling out the noise. Ensemble learning– ensemble means group or combination. Ensemble learning in the context of machine learning is referred to methods that generate many classifiers…

Big or small-let’s save them all: Uncovering the factors responsible- Multiple Regression Analysis

Multiple regression analysis is tool that allows you to expand on your research question, and conduct a more rigorous test of the association between your explanatory and response variable by adding additional quantitative and/or categorical explanatory variables to your linear regression model. I discuss this in detail in this post

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Big or small-let’s save them all: How the data was collected

For this research study, I have chosen the “Gapminder Codebook”. It combines longitudinal survey data on respondents’ social, economic, psychological and physical well-being with contextual data on the family, neighbourhood, community, school, friendships, peer groups, and romantic relationships, providing unique opportunities to study how social environments and behaviours in adolescence are linked to health and achievement…

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Big or small: let’s save them all: Exploring Statistical Interactions

Statistical interaction describes a relationship between two variables that is dependent upon, or is moderated by, a third variable. The effect of a moderating variable is often characterized statistically as an interaction. That is a third variable that affects the direction and or strength of the relation between your explanatory, or x variable, and your…