# Predictive Power Score vs CorrelationFunnel

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## Battle of the EDA Packages

**Exploratory Data Analysis (EDA)** is what every data scientist does to understand actionable insights from the data.
This process used to take forever. Not anymore. We’re kicking the tires on 2 EDA packages in a Battle Royale to
determine which one reigns supreme.

`ppsr`

– An implementation of the Predictive Power Score.`correlationfunnel`

– My R package that leverages a “binning trick” for inference using correlation.

As you follow along, you can use my Ultimate R Cheatsheet. It consolidates the most important R packages
(ones I use every day) into 1 cheatsheet. We’ll also be using the `tidyverse`

and `tidymodels`

/ `xgboost`

from the cheatsheet as the tie-breaker.

So let’s get started. You’re making these data visualizations today:

## The Drawbacks of Correlation

Did you know that you miss important insights with correlation?

Correlation plots are must-know plots. **Everyone understands correlations (even non-technical people).** Correlation are great for explaining insights in simple terms:

**Magnitude (between zero in one):**two features have a higher degree of relationship the closer the magnitude gets to one.**Sign (Positive/Negative):**A positive sign indicates a positive relationship (both go up). A negative sign indicates an inverse relationship (as one goes up, the other goes down).

**But Correlation has serious drawbacks.**

- Doesn’t work well with
**Non-Linear Relationships** - Doesn’t work on
**Categorical Data**

Enter the **Predictive Power Score**, a new technique for finding relationships.

## The Contender: Predictive Power Score

An enhanced version of the traditional correlation that scores feature importance

Predictive Power Score fixes the issues. It works with:

- Non-Linear Relationships
- Categorical Data

Here’s the implementation in the ppsr package.

This outputs a nice **“PPScore Heatmap”**, which looks very similar to a Correlation Matrix Heatmap. We can see that Tenure and TotalCharges are the top features identified with relationship to Churn.

Awesome. In a few lines of code, we got some insights telling us to look closer at TotalCharges and Tenure.

**But did the PPScore miss anything?**

Let’s try another method: My Correlation Funnel R Package.

## The Defender: Correlation Funnel

Uses the “binning trick” to fix the issues of traditional correlation

**Correlation Funnel fixes the issues with traditional correlation** using a clever binning strategy (i.e. the “binning trick”) that allows categorical data to be one-hot encoded and numeric features to be binned into binary groups.

- First
`binarize()`

the data – Note this changes the columns in your data using the “binning trick” - Then
`correlate()`

– Make sure to use the binarized target variable name (Churn__Yes) - Then visualize with
`plot_correlation_funnel()`

This creates an easy to infer plot that shows not only magnitude of relationship, but also which parts of the feature correlate most with the segment of interest (e.g. Contract= Month-to-Month correlates with Churn=Yes).

We see new features that PPScore didn’t have:

- Contract
- Online Security
- Tech Support

#### Which is right? We need a tie-breaker.

## Tie-Breaker: XGBoost + VIP

Use feature importance from a model designed for non-linear relationships

To help us decide which to believe, we can use a 3rd model – XGBOOST:

- Xgboost works well with non-linear data (tree-based, very accurate)
- We can get variable importance from Xgboost

Watch the YouTube Video for this one.

And the results are in, the top features for each method are:

### Interpretation of Results

- Each EDA method produced different results.
**My interpretation – Experiment!**Use multiple types of EDA techniques.- You learned 3 EDA Techniques in this tutorial.

## Your boss

After he sees your new EDA skills…

## But if you really want to improve your data skills…

### Here’s how to master R.

What happens after you learn R for Business from Matt.

This is career acceleration.

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