PROJECT#2 – Raisin fever --- DRAFT1
Released XXXXXXXXXX
Collaborative Assignment (groups of 2-3)
Use the AIMA-PYTHON li
ary to explore the UCI raisin dataset
You will be building classifier models, analyzing results, comparing models and offering improvements.
GOAL: Explore AIMA Python modules for classification, UCI dataset, and metrics (AIMA & custom – NO sklearn)
REQUIREMENTS:
• Create classification models using AIMA learners (simple perceptron, ensemble, neural network);
• Make use of AIMA performance metrics (E
or ratio, accuracy_score, r2_score) & custom metrics (confusion matrix &
classification report;
• Useful modules will be found in learning4e, neural_networks, deep_learning4e … ;
• Sample code can be found in learning.ipynb, test_learning, Learning_RR_modified_Perceptron, Learners… ;
• Code must be THOROUGHLY DOCUMENTED, with a complete General Header, and inline comments that will make
SEARCHING for tasks completed easy to locate. If the grader is unable to quickly locate supporting code for each task
elow, the task will get 0 points.;
• There will be a written report required, including a results table.
TASKS
• DATA
o Load UCI raisin dataset
o Assign the attribute names and source from the .txt file
o Display details about the data, using DataSet attributes & assigning known details from UCI repository
o Prepare data for training & validating models, reserving a subset of rows as a hold-out set for testing
• PERCEPTRON Classifiers
o Generate Simple PERCEPTRON classifiers & record hyperparameters & results (to table)
▪ Generate 1st model using all defaults
▪ Change the learning rate, until you see improvement
▪ Change the number of epochs, until you see improvement
o Create a final PERCEPTRON model, using best hyperparameters from above, and ALL data for training
o Record results using performance metrics, when tested on a single sample, with features: [TBA]
• ENSEMBLE modeling
o Use AdaBoost to generate ensemble models, starting with defaults.
o Record hyperparameters & results (to table)
o Record results using performance metrics, when tested on a single sample, with features: [TBA]
• NEURAL NETWORK modeling
o Generate multi-layer perceptron classifiers & record hyperparameters & results (to table)
▪ Generate 1st model using all defaults
o Record results for each of: #hidden layers/nodes is (1,10), (2,10), (3,10)
o Record results for each of: learning rate at 1.0, 0.1, .01, and <# of your choice>
o Create a final NNet model by selecting the best hyperparameters from above, and using ALL data for training
o Record results using performance metrics, when tested on a single sample, with features: [TBA]
DELIVERABLES: XXXXXXXXXXG# will be your group number.
• WRITTEN REPORT (.pdf)
o Overview describing your general approach to the problem (1paragraph);
1 This DRAFT should get you started, but be sure to watch for update(s), which will provide more detailed requirements.
https:
archive.ics.uci.edu/ml/datasets/Raisin+Dataset
o Table of results (1 row per model
• G#p2.ipynb – CLEAN source file.
o Include a general header (names, etc.) & generous comments to document your code
o You are required to include ALL dependencies in your submission.
o Remove any unnecessary code (unused functions, troubleshooting, etc.)
o Explicitly note (w/comments) any classes or functions you wrote or modified
• G#p2.pdf – .ipynb notebook,
o Visible: all code cells & comments,
o Visible: outputs from successful run & demonstrations that support your report & results table