dlpy.model.Model.plot_evaluate_res

Model.plot_evaluate_res(cas_table=None, img_type='A', image_id=None, filename=None, n_images=5, target='_label_', predicted_class=None, label_class=None, randomize=False, seed=-1)

Plot the bar chart of the classification predictions

Parameters:

cas_table : CASTable, optional

If None results from model.evaluate are used Can pass in another table that has the same prediction column names as in model.valid_res_tbl

img_type : str, optional

Specifies the type of classification results to plot * A - All type of results * C - Correctly classified results * M - Miss classified results

image_id : list or int, optional

Specifies the image by ‘_id_’ column to be displayed

filename : list of strings or string, optional

The name of a file in ‘_filename_0’ or ‘_path_’ if not unique returns multiple

n_images : int, optional

Number of images to evaluate

target : string, optional

name of column for the correct label

predicted_class : string, optional

Name of desired prediction class to plot results

label_class : string, optional

Actual target label of desired class to plot results

randomize : bool, optional

If true randomize results

seed : int, optional

Random seed used if randomize is true