trainnning time for comment
more results
all request done
complete test2
added login, sign up, getstate and get video requsts
cleaning up features format, finishing up rate video
finishing up features
recommitting
added text to map severity
modified auc
modified style
organized landing page
temporary testing phase 1
video rate page added
initial commit
added create
createtask and view task
added handleSelect to all and create new view for visual scheduling
added child select before questionnaire-QCHAT
Qchat select
request done QA chat, almost done in AQchat
added ResultDisplay to Adolescent
added result shoe for AQ adult and added CorAdult in resultdisplay to differntiate between child or adult
done AQ for all
All models with auc in temp.csv
anything
Create child account 3l website+ mn l questionnaire ll child
Upload videos to server
Questionnaire result stage
Wesbtie Questionaire
Est2bl files 3l server
Time of models
Main Page For Questionnaire on Website
Website Login and Sign Up
added picklization for features used by models, added fromratertoprediction.py given rater's answers to all features gives prediction using pickled model and pickeled features
Interface to rate new videos In python
addes docstring to all files, deleted unneeded files
Tests For Diagnosis Results
AAC
Video Rater Portal: (Login, Rating Page)
Interface to rate new videos In python
Sync with Nabil
Main Page For Questionnaire on Website
Wesbtie Questionaire
Website Login and Sign Up
Visual Schedule Start
Est2bl files 3l server
Random Forest ANOVA
Random Forest Forward Selection
modular code to pickle all best scoring models with their corresponding feature selection method
All performing model, train and save then how to predict directly
modular code to work for all models with all classifiers
added pickelization of all DTs and saved them in solved models
was able to pickle all DT and de pickle saving results (ik they are useless but why not) as well
All performing model, train and save then how to predict directly
Done first bit with DT, working on making code more modular with all DT first
Check data in paper
30 questions as multiple choice questions at seventh grade reading level 0 to 3 with scores indicating severity of autism features 8 to indicate feature can not be scored
30 questions as multiple choice questions at seventh grade reading level 0 to 3 with scores indicating severity of autism features 8 to indicate feature can not be scored
latest result sgd top 5 performing with all feature selection
Calc AUC
All performing model, train and save then how to predict directly
Check data in paper
added auc_calc for sgd top 700 with diff feature selectors
Calc AUC
Calc AUC
Calc AUC
Add decision tree visualization
Modify text according to Dr Karma's comments
Add decision tree visualization
Add random forest results
Add Feature map for best performing models and add text accordingly
Given foldername and filename visualize tree
modular visualization for DT
added Dtvisual and DTvisualforward