Plot model scores

visualizes model fined tuned scores from the cross validation

# Author: L.Kouadio
# Licence: BSD-3-clause

plot_model_scores() is able to read multiple classifiers and accepts differents way of estimators arrangements. Here is two examples of estimators arrangement before feeding to the function.

• Append scores to the model

import numpy as np
from watex.exlib.sklearn import SVC
from watex.view.mlplot import plot_model_scores
svc_model = SVC()
svc_fake_scores = np.sqrt (np.abs (np.linspace (0, 50, 200 ) + np.random.randn (200 ))) #add gaussian noises
# normalize the scores
svc_fake_scores = (svc_fake_scores - svc_fake_scores.min())/ (svc_fake_scores.max() - svc_fake_scores.min())
plot_model_scores([(svc_model, svc_fake_scores )], ** dict (xlabel ='samples', ylabel ='scores', font_size =7., lw=2. ))

• Use scores separately and customize plot by passing baseplot keyword properties

base_plot_params ={
                    'lw' :3.,
                    'ls': '-.',
                    'lc':'m',
                    'ms':7.,
                    'fig_size':(9, 6),
                    'font_size':15.,
                    'xlabel': 'samples',
                    'ylabel':'scores' ,
                    'marker':'o',
                    'alpha' :1.,
                    'yp_markeredgewidth':2.,
                    'show_grid' :True,
                    'galpha' :0.2,
                    'glw':.5,
                    'rotate_xlabel' :90.,
                    'fs' :3.,
                    's' :20 ,
                    'sns_style': 'ticks',
                }
plot_model_scores([svc_model], scores =[svc_fake_scores] , **base_plot_params )