Confusion Matrix 3D with Overall PCC and Group Statistics 1.0

% Confusion matrix-based model performance summary tool.% Works with character and numeric data, for any number of groups.%% Displays your confusion matrix as a 3D bar chart of your observations,% broken down by their actual and predicted groups.%% Takes into account the chance that your predicted and actual groups may% contain some mutually exclusive groups/classes.% Assumes that union(predicted and actual_groups) contains all% possibilities for Groups.%% Returns the overall PCC and the following stats per group:% True Positives, False Positives, True Negatives, False Negatives,% Sensitivity, Specificity, PCC.%%% OUTPUT:% 1) a 3D Bar Chart of the number of observations per group predicted as% each group (helps you visualize the performance of your model in% predicting each of several groups). X and Y tick labels are the% names (char or numeric) of your predicted and actual groups in ascending% alphanumeric order (the same order in the groups_list variable).%% 2) confusion_matrix (matrix of doubles): the counts underlying the 3D Bar% Chart confusion matrix, where columns are different predicted groups, in% ascending alphanumeric order, and rows are different actual groups, in% ascending alphanumeric order (the same order in the groups_list variable)%% 3) overall_pcc (double): the overall Percent Correctly Classified in your data%% 4) group_stats (cell array of structs), where each struct contains:% group -- the name of the group for the current stat struct% TP, FP, FN,TN -- True&False Positives&Negatives for the group% sensitivity -- TP/(TP+FP) for the group% specificity -- TN/(TN+FN) for the group% PCC -- (TP+TN)/(TP+TN+FP+FN) for the group% the cell array's structs are arranged in alphanumeric order of% group names.%% 5) groups_list (cell array of chars or vector): the names of groups in% alphanumeric order, the same order as they appear on the Confusion Matrix% 3D Bar Chart and in the group_stats cell array.%% INPUTS% parameter_name (datatype)-- description% 1) predicted_groups (vector of numeric/logicals, or cell array of chars)--% The group for each observation, as predicted by your model. If you are% using a logistic regression model, you need to translate the predicted% logit scores/ probabilities into groups, based on your own cutoff% value(s), and then feed those groups into this function.%% 2) predicted_groups (vector of numeric/logicals, or cell array of chars)--% The group for each observation, based on your actual data.%% Note: if one of these two inputs is a cell array of chars, both need to% be cell arrays of chars.

Requirements:	No special requirements
Platforms:	Matlab
Keyword:	Array,  Classified,  Correctly,  Current,  Specificity,  Struct,  Structs,  Tntn Bfn,  Tptp Bfp,  True False
Users rating:	0/10