Data visualization

Data aggregation

Data normalization

Data storage

Pie charts fail to represent proportions when categories are not mutually exclusive.

 Pie charts are less visually appealing than bar charts.

 Pie charts provide limited precision when comparing the sizes of slices.

Pie charts are not as effective as line charts for representing trends over time.

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