Use a 3D bar chart to show more complex patterns

 Increase the number of bars to include every category on the chart.

Group categories into broader categories to reduce the number of bars.

Use a pie chart instead of a bar chart.

Intervals

Range

Quantiles

Whiskers

The L1 norm adds a penalty proportional to the absolute values of the coefficients, leading to sparse solutions

The L1 norm encourages all coefficients to be close to zero but not exactly zero.

The L1 norm is less effective than the L2 norm for high-dimensional data.

The L1 norm is the sum of the squared values of the coefficients

Recall measures the percentage of actual positive instances correctly identified by the model.

Recall is the same as specificity, which measures the rate of true negatives.

Recall focuses on minimizing false positives.

 Recall penalizes models that miss true positive cases.

The dataset has a large number of irrelevant features.

The data is not linearly separable, and PCA is not useful.

The data contains too many missing values for PCA to be applied effectively.

Most of the information in the data is concentrated in a few components, so dimensionality reduction is effective