(y - y`)2 / 2N

(y - y`) / 2N

(y - y`)

(y - y`) / N

Replace missing values with the mode (most frequent value) of the column

Replace missing values with the mean of the column.

Replace missing values with random noise

Impute missing values using linear regression

Model deployment

Data cleaning

Model evaluation

Feature engineering

Data aggregation

Data storage

Data normalization

Data visualization

Intervals

Quantiles

Range

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

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

Use a pie chart instead of a bar chart.

Use a 3D bar chart to show more complex patterns

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

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.

Recall focuses on minimizing false positives.

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 penalizes models that miss true positive cases.

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

The dataset has a large number of irrelevant features.

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

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