HTML Subpages

Overview

Overview subpage gives general information on the Features - their summary statistics, number of missing values, etc. (similar to describe method from pandas.DataFrame).

If the number of features isn’t too big, seaborn pairplot is also included.

Note

Feature names in all tables are ‘hoverable’ - upon hovering, corresponding description and mapping will be shown.

Features View

Features subpage allows you to dive deeper into each Feature present in your data. Not only summary statistics and underlying distribution are included, but also transformations and correlations with other features - feature engineering might be a little bit easier. To change displayed feature, you can click on the ‘burger’ button in the upper left corner and choose another one from the opened menu.

Note

Depending on the number of features, refreshing the page upon feature selection change might take a while - please be patient!

Content in Features subpage is divided into subsections that you can easily expand or hide (if the page gets too cluttered). First two sections provide information on a chosen feature (both original and transformed), whereas the third section shows correlations between all features (normalized and original), regardless of selection.

Note

When Numerical Feature is selected, Transformations sections will show plots of Normal Transformations. This should help you make a conscious decision which of the Transformers is the best for that feature (per sklearn guidance).

ScatterPlot Grid is included in the last section - Bokeh visualization that plots every feature against another as scatter plots in a manner similar to seaborn pairplot, but with a twist added - every feature is also used as a hue (coloring). The idea behind it was to provide visual assistance with manual feature engineering (if there are any easily separable groups).

Note

Row with a chosen Feature on X axis and coloring by the same Feature is greyed out to minimize confusion (ideally separate colored groups would be present). Greying out was included as an alternative to removing that row, as it was technically difficult to not break the whole structure of the Grid while doing that.

Models

Models subpage gives information on provided Models performance. Shown results and visualizations will always correspond to the 3 best scoring Models chosen during HTML Dashboard creation (search methods). Even though provided plots will be different based on a problem type, scoring table in the upper left corner and predictions table at the bottom stay the same.

Note

Model names are ‘hoverable’ in a similar manner to Feature Names in Overview subpage - the difference being that instead of descriptions, used params are shown.

Classification

Models subpage in classification problem generates Plots of performance curves (ROC curve, precision-recall curve and detection error tradeoff curve) and Confusion Matrices for every model (see sklearn roc, precision-recall, detection error tradeoff).

Note

Legend is interactive - you can mute lines by clicking at a particular legend entry.

Regression

Models subpage in regression problem generates two Plots for every Model: prediction errors and residuals.

• prediction errors plot shows Actual Target on X axis and Predicted Target on Y Axis;

• residuals plot shows Actual Target on X axis and the difference between Predicted and Actual on Y Axis.

Multiclass

Models subpage in multiclass problem shows you Confusion Matrices for every Model assessed.