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---
author: Claudio Maggioni
title: Information Modelling & Analysis -- Project 2
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geometry: margin=2cm
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---
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# Code Repository
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The code and result files, part of this submission, can be found at:
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- Repository: [https://github.com/infoMA2023/project-02-bug-prediction-maggicl ](https://github.com/infoMA2023/project-02-bug-prediction-maggicl )
- Commit ID: **TBD**
# Data Pre-Processing
I use the sources of the [Closure]{.smallcaps} repository that were already downloaded using the command:
```shell
defects4j checkout -p Closure -v 1f -w ./resources
```
and used the code in the following subfolder for the project:
```
./resources/defects4j-checkout-closure-1f/src/com/google/javascript/jscomp/
```
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relative to the root folder of the repository. The resulting CSV of extracted, labelled feature vectors can be found in
the repository at the path:
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```
./metrics/feature_vectors_labeled.csv
```
relative to the root folder of the repository.
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Unlabeled feature vectors can be computed by running the script `./extract_feature_vectors.py` .
The resulting CSV of unlabeled feature vectors is located in `./metrics/feature_vectors.csv` .
Labels for feature vectors can be computed by running the script `./label_feature_vectors.py` .
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## Feature Vector Extraction
I extracted **291** feature vectors in total. Aggregate metrics
about the extracted feature vectors, i.e. the distribution of the values of each
code metric, can be found in Table [1 ](#tab:metrics ){reference-type="ref"
reference="tab:metrics"}.
::: {#tab:metrics}
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| **Metric** | **Minimum** | **Average** | **Maximum** |
|:------|-:|---------:|-----:|
| `BCM` |0| 13.4124 | 221 |
| `CPX` |0| 5.8247 | 96 |
| `DCM` |0| 4.8652 |176.2 |
| `EX` |0| 0.1134 | 2 |
| `FLD` |0| 6.5773 | 167 |
| `INT` |0| 0.6667 | 3 |
| `MTH` |0| 11.6529 | 209 |
| `NML` |0| 13.5622 | 28 |
| `RET` |0| 3.6735 | 86 |
| `RFC` |0| 107.2710 | 882 |
| `SZ` |0| 18.9966 | 347 |
| `WRD` |0| 314.4740 | 3133 |
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: Distribution of values for each extracted code metric.
:::
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## Feature Vector Labelling
After feature vectors are labeled, I determine that the dataset contains
**75** buggy classes and **216** non-buggy classes.
# Classifiers
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<!-- In every subsection below, describe in a concise way which different
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hyperparameters you tried for the corresponding classifier, and report
the corresponding precision, recall and F1 values (for example in a
table or an [itemize]{.smallcaps}-environment). Furthermore, for every
type of classifiers, explicitly mention which hyperparameter
configuration you chose (based on above reported results) to be used in
further steps, and (in one or two sentences), explain why these
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hyperparameters may outperform the other ones you tested..-->
In this section I explain how I define and perform training for each classifier.
Since the dataset has an unbalanced number of feature vectors of each class, in order
to increase classification performance I upsample the dataset by performing sampling with
replacement over the least frequent class until the number of feature vectors matches the
most frequest class[^1].
[^1]: Upsampling due to unbalanced classes was suggested by *Michele Cattaneo* , who is attending this class.
Other than for the `GaussianNB` (Naive Bayes) classifier, the classifiers chosen for the
project offer to select hyperparameter values. In order to choose them, I perform a grid
search over each classifier. The hyperparameter values I have considered in the grid search
for each classifier are the following:
- For *DecisionTreeClassifier* :
| **Parameter** | **Values** |
|:------------|:--------------|
| criterion | gini, entropy |
| splitter | best, random |
- For *SVC* :
| **Parameter** | **Values** |
|:------------|:---------------------------|
| kernel | linear, poly, rbf, sigmoid |
| gamma | scale, auto |
- For *MLPClassifier* :
| **Parameter** | **Values** |
|:------------|:---------------------------|
| max_iter | 500000 |
| hidden_layer_sizes | $[5, 10, 15, ..., 100]$, $[15, 30, 45, 60, 75, 90]^2$, $[20, 40, 60, 80, 100]^3$ |
| activation | identity, logistic, tanh, relu |
| solver | lbfgs, sgd, adam |
| learning_rate | constant, invscaling, adaptive |
Note that the $[...]^2$ denotes a cartesian product of the array with itself, and $[...]^3$
denotes the cartesian product of $[...]^2$ with the array (i.e. $[...]^3 = [...]^2 \times [...] = ([...] \times [...]) \times [...]$).
Note also the high upper bound on iterations (500000). This is to allow convergence of the less optimal hyperparameter configurations and avoid `ConvergenceWarning` errors.
- For *RandomForestClassifier* :
| **Parameter** | **Values** |
|:-------------|:-----------------------------|
| criterion | gini, entropy |
| max_features | sqrt, log2 |
| class_weight | balanced, balanced_subsample |
The script `./train_classifiers.py` , according to the random seed $3735924759$, performs upscaling of the dataset and the grid search training, by recording precision, accuracy, recall and the F1 score of each configuration of hyperparameters. These metrics are then collected and stored in `./models/models.csv` .
The metrics for each classifier and each hyperparameter configuration in decreasing order of
accuracy are reported in the following sections.
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For each classifier, I then choose the hyperparameter configuration with highest accuracy. Namely, these configurations are:
| **Classifier** | **Hyper-parameter configuration** | **Precision** | **Accuracy** | **Recall** | **F1 Score** |
|:----|:--------|-:|-:|-:|--:|
| DecisionTreeClassifier | `criterion` : gini, `splitter` : best | 0.7885 | 0.8506 | 0.9535 | 0.8632 |
| GaussianNB | -- | 0.8 | 0.6782 | 0.4651 | 0.5882 |
| MLPClassifier | `activation` : logistic, `hidden_layer_sizes` : (60, 80, 100), `learning_rate` : constant, `max_iter` : 500000, `solver` : lbfgs | 0.8958 | 0.9425 | 1 | 0.9451 |
| RandomForestClassifier | `class_weight` : balanced, `criterion` : gini, `max_features` : sqrt | 0.8367 | 0.8851 | 0.9535 | 0.8913 |
| SVC | `gamma` : scale, `kernel` : rbf | 0.7174 | 0.7356 | 0.7674 | 0.7416 |
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## Decision Tree (DT)
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| criterion | splitter | precision | accuracy | recall | f1 |
|:------------|:-----------|------------:|-----------:|---------:|---------:|
| gini | best | 0.788462 | 0.850575 | 0.953488 | 0.863158 |
| gini | random | 0.784314 | 0.83908 | 0.930233 | 0.851064 |
| entropy | random | 0.736842 | 0.816092 | 0.976744 | 0.84 |
| entropy | best | 0.745455 | 0.816092 | 0.953488 | 0.836735 |
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## Naive Bayes (NB)
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| precision | accuracy | recall | f1 |
|------------:|-----------:|---------:|---------:|
| 0.8 | 0.678161 | 0.465116 | 0.588235 |
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## Support Vector Machine (SVP)
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| gamma | kernel | precision | accuracy | recall | f1 |
|:--------|:---------|------------:|-----------:|---------:|---------:|
| scale | rbf | 0.717391 | 0.735632 | 0.767442 | 0.741573 |
| scale | linear | 0.75 | 0.735632 | 0.697674 | 0.722892 |
| auto | linear | 0.75 | 0.735632 | 0.697674 | 0.722892 |
| auto | rbf | 0.702128 | 0.724138 | 0.767442 | 0.733333 |
| scale | sigmoid | 0.647059 | 0.678161 | 0.767442 | 0.702128 |
| auto | sigmoid | 0.647059 | 0.678161 | 0.767442 | 0.702128 |
| auto | poly | 0.772727 | 0.643678 | 0.395349 | 0.523077 |
| scale | poly | 0.833333 | 0.597701 | 0.232558 | 0.363636 |
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## Multi-Layer Perceptron (MLP)
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For sake of brevity, only the top 100 results by accuracy are shown.
| activation | hidden_layer_sizes | learning_rate | max_iter | solver | precision | accuracy | recall | f1 |
|:-------------|:---------------------|:----------------|-----------:|:---------|------------:|-----------:|---------:|---------:|
| logistic | (60, 80, 100) | constant | 500000 | lbfgs | 0.895833 | 0.942529 | 1 | 0.945055 |
| logistic | (40, 80, 100) | adaptive | 500000 | lbfgs | 0.86 | 0.91954 | 1 | 0.924731 |
| tanh | (40, 80, 100) | invscaling | 500000 | adam | 0.86 | 0.91954 | 1 | 0.924731 |
| tanh | (60, 100, 80) | adaptive | 500000 | lbfgs | 0.86 | 0.91954 | 1 | 0.924731 |
| tanh | (100, 60, 20) | constant | 500000 | adam | 0.86 | 0.91954 | 1 | 0.924731 |
| tanh | (100, 80, 80) | constant | 500000 | adam | 0.86 | 0.91954 | 1 | 0.924731 |
| relu | (75, 30) | adaptive | 500000 | lbfgs | 0.86 | 0.91954 | 1 | 0.924731 |
| logistic | (20, 40, 60) | adaptive | 500000 | lbfgs | 0.875 | 0.91954 | 0.976744 | 0.923077 |
| logistic | (40, 60, 80) | adaptive | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| logistic | (80, 40, 20) | constant | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | 30 | invscaling | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | 60 | constant | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | 85 | adaptive | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (30, 30) | constant | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (45, 45) | adaptive | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (60, 60) | invscaling | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (75, 45) | invscaling | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (75, 75) | adaptive | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (90, 90) | invscaling | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (20, 40, 60) | invscaling | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (20, 100, 20) | invscaling | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (40, 20, 100) | constant | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (40, 80, 60) | invscaling | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (40, 80, 100) | adaptive | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (60, 20, 40) | adaptive | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (60, 60, 80) | constant | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (60, 80, 80) | adaptive | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (80, 20, 40) | adaptive | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (80, 40, 80) | constant | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (80, 60, 60) | constant | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| relu | (20, 20, 80) | constant | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| relu | (20, 40, 100) | constant | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| relu | (20, 60, 20) | adaptive | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| relu | (20, 60, 100) | adaptive | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| relu | (20, 100, 20) | constant | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| relu | (20, 100, 40) | adaptive | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| relu | (40, 20, 80) | constant | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| relu | (40, 80, 60) | constant | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| relu | (60, 20, 100) | constant | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| relu | (80, 20, 60) | constant | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| relu | (80, 60, 20) | adaptive | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| relu | (100, 20, 60) | invscaling | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| logistic | (20, 60, 80) | invscaling | 500000 | lbfgs | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| logistic | (60, 20, 20) | adaptive | 500000 | lbfgs | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| tanh | (15, 45) | constant | 500000 | lbfgs | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| tanh | (45, 90) | invscaling | 500000 | lbfgs | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| tanh | (90, 30) | constant | 500000 | lbfgs | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| tanh | (20, 80, 100) | invscaling | 500000 | adam | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| tanh | (20, 80, 100) | adaptive | 500000 | lbfgs | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| tanh | (40, 40, 40) | adaptive | 500000 | lbfgs | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| tanh | (40, 60, 100) | adaptive | 500000 | adam | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| tanh | (60, 80, 60) | constant | 500000 | lbfgs | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| tanh | (100, 40, 60) | invscaling | 500000 | lbfgs | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| tanh | (100, 80, 100) | adaptive | 500000 | adam | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| relu | (30, 30) | adaptive | 500000 | lbfgs | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| relu | (20, 20, 40) | adaptive | 500000 | lbfgs | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| relu | (20, 40, 40) | adaptive | 500000 | adam | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| relu | (40, 20, 100) | adaptive | 500000 | adam | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| relu | (60, 80, 20) | invscaling | 500000 | lbfgs | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| logistic | (40, 80, 60) | adaptive | 500000 | lbfgs | 0.87234 | 0.908046 | 0.953488 | 0.911111 |
| logistic | 35 | adaptive | 500000 | lbfgs | 0.826923 | 0.896552 | 1 | 0.905263 |
| logistic | (15, 60) | invscaling | 500000 | lbfgs | 0.826923 | 0.896552 | 1 | 0.905263 |
| logistic | (45, 45) | constant | 500000 | lbfgs | 0.826923 | 0.896552 | 1 | 0.905263 |
| logistic | (20, 20, 60) | adaptive | 500000 | lbfgs | 0.826923 | 0.896552 | 1 | 0.905263 |
| logistic | (60, 60, 80) | adaptive | 500000 | lbfgs | 0.826923 | 0.896552 | 1 | 0.905263 |
| logistic | (80, 40, 100) | invscaling | 500000 | lbfgs | 0.826923 | 0.896552 | 1 | 0.905263 |
| logistic | (100, 100, 100) | constant | 500000 | lbfgs | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | 60 | constant | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (15, 15) | invscaling | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (15, 45) | adaptive | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (30, 30) | invscaling | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (30, 60) | constant | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (60, 90) | invscaling | 500000 | lbfgs | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (75, 15) | constant | 500000 | lbfgs | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (75, 45) | constant | 500000 | lbfgs | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (90, 15) | adaptive | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (90, 45) | invscaling | 500000 | lbfgs | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (20, 40, 20) | invscaling | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (20, 40, 40) | invscaling | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (20, 60, 20) | adaptive | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (20, 80, 60) | constant | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (20, 80, 80) | constant | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (20, 80, 100) | constant | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (40, 20, 60) | invscaling | 500000 | lbfgs | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (40, 60, 60) | constant | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (40, 60, 60) | invscaling | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (40, 80, 20) | adaptive | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (40, 100, 60) | constant | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (60, 40, 20) | constant | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (60, 40, 40) | invscaling | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (60, 40, 80) | constant | 500000 | lbfgs | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (60, 60, 20) | constant | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (60, 80, 60) | constant | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (60, 80, 80) | invscaling | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (60, 100, 20) | invscaling | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (60, 100, 40) | adaptive | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (60, 100, 60) | constant | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (60, 100, 60) | adaptive | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (60, 100, 80) | constant | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (80, 40, 40) | constant | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
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## Random Forest (RF)
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| criterion | class_weight | max_features | precision | accuracy | recall | f1 |
|:------------|:-------------------|:---------------|------------:|-----------:|---------:|---------:|
| gini | balanced | sqrt | 0.836735 | 0.885057 | 0.953488 | 0.891304 |
| entropy | balanced | sqrt | 0.807692 | 0.873563 | 0.976744 | 0.884211 |
| gini | balanced_subsample | sqrt | 0.807692 | 0.873563 | 0.976744 | 0.884211 |
| entropy | balanced_subsample | sqrt | 0.807692 | 0.873563 | 0.976744 | 0.884211 |
| gini | balanced | log2 | 0.82 | 0.873563 | 0.953488 | 0.88172 |
| entropy | balanced | log2 | 0.82 | 0.873563 | 0.953488 | 0.88172 |
| gini | balanced_subsample | log2 | 0.803922 | 0.862069 | 0.953488 | 0.87234 |
| entropy | balanced_subsample | log2 | 0.803922 | 0.862069 | 0.953488 | 0.87234 |
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# Evaluation
## Output Distributions
Add a boxplot showing mean and standard deviation for **Precision**
values on all 6 classifiers (5 trained + 1 biased)\
Add a boxplot showing mean and standard deviation for **Recall** values
on all 6 classifiers (5 trained + 1 biased)\
Add a boxplot showing mean and standard deviation for **F1** values on
all 6 classifiers (5 trained + 1 biased)
{#fig:boxplot}
## Comparison and Significance
For every combination of two classifiers and every performance metric
(precision, recall, f1) compare which algorithm performs better, by how
much, and report the corresponding p-value in the following
subsubsections:
::: {#tab:precision}
| | DecisionTreeClassifier | GaussianNB | MLPClassifier | RandomForestClassifier | SVC |
|:-----------------------|:-------------------------|:-------------|:----------------|:-------------------------|------:|
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| BiasedClassifier | 0.0000 | 0.0000 | 0.0000 | 0.0000 |0.0000|
| DecisionTreeClassifier | -- | 0.0893 | 0.4012 | 0.0000 |0.0000|
| GaussianNB | -- | -- | 0.0348 | 0.0000 |0.0000|
| MLPClassifier | -- | -- | -- | 0.0000 |0.0000|
| RandomForestClassifier | -- | -- | -- | -- |0.0000|
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: Pairwise Wilcoxon test for precision for each combination of classifiers.
:::
::: {#tab:recall}
| | DecisionTreeClassifier | GaussianNB | MLPClassifier | RandomForestClassifier | SVC |
|:-----------------------|:-------------------------|:-------------|:----------------|:-------------------------|------:|
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| BiasedClassifier | 0.0000 | 0.0000 | 0.0000 | 0.0000 |0.0000|
| DecisionTreeClassifier | -- | 0.0000 | 0.0118 | 0.3276 |0.0000|
| GaussianNB | -- | -- | 0.0000 | 0.0000 |0.0000|
| MLPClassifier | -- | -- | -- | 0.0001 |0.0000|
| RandomForestClassifier | -- | -- | -- | -- |0.0000|
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: Pairwise Wilcoxon test for recall for each combination of classifiers.
:::
::: {#tab:f1}
| | DecisionTreeClassifier | GaussianNB | MLPClassifier | RandomForestClassifier | SVC |
|:-----------------------|:-------------------------|:-------------|:----------------|:-------------------------|------:|
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| BiasedClassifier | 0.0000 | 0.0000 | 0.0000 | 0.0000 |0.0000|
| DecisionTreeClassifier | -- | 0.0000 | 0.4711 | 0.0000 |0.0000|
| GaussianNB | -- | -- | 0.0000 | 0.0000 |0.0000|
| MLPClassifier | -- | -- | -- | 0.0000 |0.0000|
| RandomForestClassifier | -- | -- | -- | -- |0.0000|
2023-05-24 12:06:24 +00:00
: Pairwise Wilcoxon test for the F1 score metric for each combination of classifiers.
:::
### F1 Values
-
- \...
### Precision
(same as for F1 above)
### Recall
(same as for F1 above)
## Practical Usefulness
Discuss the practical usefulness of the obtained classifiers in a
realistic bug prediction scenario (1 paragraph).
