Full list of methods available (Version 0.1.0)
Although the following list of methods is given per module basis, that is just to reflect the separate focus areas. Keeping with the goal of simple API, all of these modules are internally inherited in the main class.
So, an user can just declare model = mlr()
, fit the model with some data, model.fit(X,y)
and then call any of the following methods on the same object model
. You do not need to call separate modules, at this point.
Data_plots
module

corrplot()
: Creates a heatmap of the correlation matrix 
pairplot()
: Creates pairplot of all variables and the target using theSeaborn
library 
plot_fitted()
: Plots fitted values against the true output values from the data
Diagnostics_plots
module

fitted_vs_residual()
: lots fitted values vs. residuals 
fitted_vs_features()
: Plots residuals vs all feature variables in a grid 
histogram_resid()
: Plots a histogram of the residuals (by default, normalized) 
qqplot_resid()
: Creates a quantilequantile (QQ) plot for (standardized) residuals comparing with a normal distribution 
shapiro_test()
: Performs ShapiroWilk normality test on the residuals (by default, normalized)
Inference
module

std_err()
: Returns standard error values of the features after fitting regression model 
pvalues()
: Returns pvalues of the features after fitting regression model 
tvalues()
: Returns ttest statistics of the features after fitting regression model 
ftest()
: Returns the Fstatistic of the overall regression and corresponding pvalue (as a tuple) 
conf_int()
: Computes the confidence interval for the given variable(s), passed on ascols
. Default confidence level is set at 0.05, which can be changed by the user usingalpha
argument.
Metrics
module

sse()
: Returns sum of squared errors (model vs. actual) 
sst()
: Returns total sum of squared errors (actual vs avg(actual)) 
r_squared()
: Returns calculated value of r^2 (coefficient of regression) 
adj_r_squared()
: Returns calculated value of adjusted r^2 
mse()
: Returns calculated value of meansquare error (MSE) 
aic()
: Returns AIC (Akaike information criterion) 
bic()
: Returns BIC (Bayesian information criterion) 
print_metrics()
: Prints a report of the useful metrics for a given model object 
summary_metrics()
: Returns a dictionary of the useful metrics
Multicollinearity
module

corrcoef()
: Returns the correlation coefficient matrix for the features 
covar()
: Returns the covariance matrix for the features 
vif()
: Computes variance influence factors for each feature variable
Outliers
module

cook_distance()
: Computes and plots Cook's distance 
influence_plot()
: Creates the influence plot 
leverage_resid_plot()
: Plots leverage vs normalized residuals' square
More features will be added in the future releases!