1. Machine learning Empirical Performance Analysis
Elizabeth Daudelin

2. Project Algorithms 52 data sets
Decision Tree, Random Forest, Gradient Boosting, CNN
Parameters to vary: tree depth, # of trees
52 data sets
Wide range of sizes (e.g.195 x 22 and 7,352 x 561)
Each with a set of true labels, as well as 10 sets of randomized labels

3. Tree Results Tree Depth Decision Tree # of Trees Random Forest2 4 8
Decision Tree
0.8234
0.8402
0.8457 10
100
1000
0.8751
0.8882
0.8896
0.8443
0.8612
0.8616
0.8765
0.8829
0.8715
0.8820
0.8833
0.8721
# of Trees
Random Forest
Gradient Boosting

4. Tree Results (TRUeClass only)
Tree Depth 2 4 8
Decision Tree
0.8270
0.8390
0.8379 10
100
1000
0.8757
0.8892
0.8916
0.8452
0.8552
0.8530
0.8784
0.8808
0.8632
0.8795
0.8843
0.8643
# of Trees
Random Forest
Gradient Boosting

5. CNN
Input: d x 1
3 x 1 Convolution(15 filters) Relu output x 1 Avg Pooling x 1 Convolution Relu output Flatten Dense(# of classes) Softmax
Result: 72% average accuracy