# All metrics

List of all available Metrics and Charts.

## CSV summaryâ€‹

- Number of variables
- Number of observations
- Number of missing values
- Percentage of missing values
- Number of duplicated rows
- Percentage of duplicated rows
- Number of
**numerical**variables - Number of
**categorical**variables - Number of
**datetime**variables

Summary with all variable name and type (float, int, string, datetime).

## Data qualityâ€‹

**Numerical**variables- Average
- Standard deviation
- Minimum
- Maximum
- Percentile 25%
- Median
- Percentile 75%
- Number of missing values
- Histogram with 10 bins

**Categorical**variables- Number of missing values
- Percentage of missing values
- Number of distinct values
- For each distinct value:
- count of observations
- percentage of observations

**Ground truth**- if categorical i.e. for a classification model: bar plot
*(for both reference and current for an easy comparison)* - if numerical, i.e. for a regression model: histogram with 10 bins
*(for both reference and current for an easy comparison)*

- if categorical i.e. for a classification model: bar plot

## Model qualityâ€‹

- Classification model
- Number of classes
- Accuracy
*(for both reference and current for an easy comparison)* - Line chart of accuracy over time
- Confusion matrix
- Log loss,
*only for binary classification at the moment* - Line chart of log loss over time,
*only for binary classification at the moment* - For each class:
- Precision
*(for both reference and current for an easy comparison)* - Recall
*(for both reference and current for an easy comparison)* - F1 score
*(for both reference and current for an easy comparison)* - True Positive Rate
*(for both reference and current for an easy comparison)* - False Positive Rate
*(for both reference and current for an easy comparison)* - Support
*(for both reference and current for an easy comparison)*

- Precision

- Regression model
- Mean squared error
*(for both reference and current for an easy comparison)* - Root mean squared error
*(for both reference and current for an easy comparison)* - Mean absolute error
*(for both reference and current for an easy comparison)* - Mean absolute percentage error
*(for both reference and current for an easy comparison)* - R-squared
*(for both reference and current for an easy comparison)* - Adjusted R-squared
*(for both reference and current for an easy comparison)* - Variance
*(for both reference and current for an easy comparison)* - Line charts for all of the above over time
- Residual analysis:
- Correlation prediction/ground_truth
- Residuals plot, i.e, scatter plot for standardised residuals and predictions
- Scatter plot for predictions vs ground truth and linear regression line
- Histogram of the residuals
- Kolmogorov-Smirnov test of normality for residuals

- Mean squared error

## Data Driftâ€‹

Data drift for all features using different algorithms depending on the data type: float, int, categorical. We use the following algorithms (but others will be added in the future):