This post follows the R : Exploring Data for Machine Learning Modeling post, exploring linear model in the context of machine learning.

We'll still use the Wage dataset from the ISLR package.
This dataset reports wage and other data (age, education, jobclass, etc.) for a group of 3000 male workers in …

These are my notes on the Practical Machine Learning course (Week2: Plotting Predictors - Tutorial).

When exploring data for Machine Learning, we're looking for:

• imbalance outcomes/predictors
• outliners
• groups of outcome points not explained by any of the predictors
• skewed variables (that needs to be transformed)

We'll use the Wage dataset …

The Central Limit Theorem states that the distribution of sample statistics (e.g. mean) is approximatively normal, regardless of the underlying distribution, with mean = \(\mu\) and variance = \(\sigma^2\) ...

This is my note on swirl course Regression Model : Overfitting and Underfitting.

Definition¶

A variance inflation factor (VIF) is a ratio of estimated variances, the variance due to including the ith regressor, divided by that due to including a corresponding ideal regressor which is uncorrelated with the others. VIF is …

Exploring multivariable regression with the hunger dataset

Plotting the earthquakes of the last 30 months with R and the the National Earthquake Hazards Reduction Program (NEHRP) feed ...

Synopsis¶

This analysis involves exploring the U.S. National Oceanic and Atmospheric Administration's (NOAA) storm database.

This database tracks characteristics of major storms and weather events in the United States, including when and where they occur, as well as estimates of any fatalities, injuries, and property damage.

This analysis focuses …

Experimenting the Cental Limit Theorem with R