all done but practical usefulness
This commit is contained in:
parent
5841b222ee
commit
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6 changed files with 282 additions and 261 deletions
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@ -4,6 +4,7 @@ import pandas as pd
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import glob
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import re
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import itertools
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import statsmodels.stats.power as pw
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import numpy as np
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from train_classifiers import perform_grid_search, load_dataset
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import seaborn as sns
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@ -110,6 +111,17 @@ def main():
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df_stats = pd.DataFrame(columns=['classifier_a', 'classifier_b', 'metric', 'pvalue'])
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short_names = {
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'MLPClassifier': "MLP",
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'GaussianNB': "NB",
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'SVC': "SVP",
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'DecisionTreeClassifier': "DT",
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'RandomForestClassifier': "RF",
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'BiasedClassifier': 'Biased'
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}
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observations = {}
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i = 1
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for classifier_a in classifier_list:
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for classifier_b in classifier_list:
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@ -128,9 +140,43 @@ def main():
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df_stats.loc[i, 'classifier_a'] = classifier_a
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df_stats.loc[i, 'classifier_b'] = classifier_b
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df_stats.loc[i, 'metric'] = metric
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df_stats.loc[i, 'pvalue'] = wilcoxon(series_a, series_b).pvalue
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pvalue = wilcoxon(series_a, series_b).pvalue
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df_stats.loc[i, 'pvalue'] = pvalue
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if metric not in observations:
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observations[metric] = []
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sa = short_names[classifier_a]
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sb = short_names[classifier_b]
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meana = np.round(np.mean(series_a), decimals=4)
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meanb = np.round(np.mean(series_b), decimals=4)
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pvalue = np.round(pvalue, decimals=4)
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s = f"- Mean *{metric}* for *{sa}*: {meana},"
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s += f" mean *{metric}* for *{sb}*: {meanb} $\\Rightarrow$ "
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if pvalue < 0.05:
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better = sa if meana > meanb else sb
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worse = sa if better == sb else sb
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s += f"*{better}* is better than *{worse}* (*p-value* $= {pvalue}$)"
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else:
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eff_size = (np.mean(series_a) - np.mean(series_b)) / np.sqrt((np.std(series_a) ** 2 + np.std(series_b) ** 2) / 2.0)
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power = pw.FTestAnovaPower().solve_power(effect_size=eff_size, nobs=len(series_a) + len(series_b), alpha=0.05)
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power = np.round(power, decimals=4)
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if power >= 0.8:
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s += f"*{sa}* is as effective as *{sb}* (*p-value* $= {pvalue}$, *5% corrected ANOVA power* $= {power}$)"
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else:
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s += f"statistical test inconclusive (*p-value* $= {pvalue}$, *5% corrected ANOVA power* $= {power}$)"
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i += 1
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observations[metric].append(s)
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for metric in metric_list:
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if metric == 'accuracy':
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continue
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print(metric + ":")
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print("\n".join(observations[metric]))
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df_stats.to_csv(OUT_DIR + '/model_stats.csv')
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@ -169,6 +215,8 @@ def main():
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dftab.columns = [x[1] if x[0] == 'value' else x[0] for x in dftab.columns]
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print(dftab.to_markdown(index=False))
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print()
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if __name__ == '__main__':
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main()
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@ -6,7 +6,7 @@
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|
||||
7,DecisionTreeClassifier,RandomForestClassifier,precision,8.283473187323235e-12
|
||||
8,DecisionTreeClassifier,RandomForestClassifier,recall,0.3276029575034267
|
||||
9,DecisionTreeClassifier,RandomForestClassifier,f1,1.4515097813437996e-10
|
||||
10,DecisionTreeClassifier,SVC,precision,6.472995016722292e-16
|
||||
11,DecisionTreeClassifier,SVC,recall,3.864155888689142e-18
|
||||
12,DecisionTreeClassifier,SVC,f1,3.896559845095909e-18
|
||||
13,GaussianNB,MLPClassifier,precision,0.03476088049603166
|
||||
14,GaussianNB,MLPClassifier,recall,3.873544128513129e-18
|
||||
15,GaussianNB,MLPClassifier,f1,3.896120241954008e-18
|
||||
16,GaussianNB,RandomForestClassifier,precision,5.027978595522601e-10
|
||||
17,GaussianNB,RandomForestClassifier,recall,3.8656827355135645e-18
|
||||
18,GaussianNB,RandomForestClassifier,f1,3.896120241954008e-18
|
||||
19,GaussianNB,SVC,precision,7.361006463422299e-13
|
||||
20,GaussianNB,SVC,recall,3.881639684405151e-18
|
||||
21,GaussianNB,SVC,f1,4.265842540306607e-18
|
||||
22,MLPClassifier,RandomForestClassifier,precision,2.9302015489842885e-09
|
||||
23,MLPClassifier,RandomForestClassifier,recall,0.00010909237805840521
|
||||
24,MLPClassifier,RandomForestClassifier,f1,1.1542838431590428e-11
|
||||
25,MLPClassifier,SVC,precision,3.6744416439536415e-16
|
||||
26,MLPClassifier,SVC,recall,5.645631221640026e-18
|
||||
27,MLPClassifier,SVC,f1,5.112831740936498e-18
|
||||
28,RandomForestClassifier,SVC,precision,4.0161556854627e-18
|
||||
29,RandomForestClassifier,SVC,recall,3.8584897469079895e-18
|
||||
30,RandomForestClassifier,SVC,f1,3.896559845095909e-18
|
||||
31,BiasedClassifier,DecisionTreeClassifier,precision,3.881858705649312e-18
|
||||
32,BiasedClassifier,DecisionTreeClassifier,recall,1.0267247842714985e-14
|
||||
33,BiasedClassifier,DecisionTreeClassifier,f1,3.881858705649312e-18
|
||||
34,BiasedClassifier,GaussianNB,precision,3.876167958900271e-18
|
||||
35,BiasedClassifier,GaussianNB,recall,3.7861845925093915e-18
|
||||
36,BiasedClassifier,GaussianNB,f1,4.22499456189738e-16
|
||||
37,BiasedClassifier,MLPClassifier,precision,3.887338045617697e-18
|
||||
38,BiasedClassifier,MLPClassifier,recall,2.2417567344517687e-15
|
||||
39,BiasedClassifier,MLPClassifier,f1,3.886680137925553e-18
|
||||
40,BiasedClassifier,RandomForestClassifier,precision,3.8798879361158955e-18
|
||||
41,BiasedClassifier,RandomForestClassifier,recall,4.647240654275836e-14
|
||||
42,BiasedClassifier,RandomForestClassifier,f1,3.8798879361158955e-18
|
||||
43,BiasedClassifier,SVC,precision,3.890848669938197e-18
|
||||
44,BiasedClassifier,SVC,recall,3.803119001919453e-18
|
||||
45,BiasedClassifier,SVC,f1,4.337936940303975e-17
|
||||
1,BiasedClassifier,SVC,f1,4.337936940303975e-17
|
||||
|
|
|
|||
|
108
report/main.md
108
report/main.md
|
|
@ -308,10 +308,19 @@ For sake of brevity, only the top 100 results by accuracy are shown.
|
|||
| gini | balanced_subsample | log2 | 0.803922 | 0.862069 | 0.953488 | 0.87234 |
|
||||
| entropy | balanced_subsample | log2 | 0.803922 | 0.862069 | 0.953488 | 0.87234 |
|
||||
|
||||
|
||||
|
||||
# Evaluation
|
||||
|
||||
To evaluate the performance of each selected classifier, each model has trained 100 times by repeating a 5-fold
|
||||
cross validation procedure 20 times. A graphical and statistical analysis to compare the distribution of
|
||||
performance metrics (precision, recall and F1 score) follows.
|
||||
|
||||
Numeric tables, statistical analysis conclusions and the boxplot diagram shown in this section are obtained by
|
||||
running the script:
|
||||
|
||||
```
|
||||
./evaluate_classifiers.py
|
||||
```
|
||||
|
||||
## Output Distributions
|
||||
|
||||
A boxplot chart to show the distribution of each of precision, recall, and F1 score
|
||||
|
|
@ -348,58 +357,65 @@ table summing up mean and standard deviation of each metric.
|
|||
|
||||
## 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:
|
||||
Given the distribution of metrics presented in the previous section, I perform a statistical analysis
|
||||
using the Wilixcon paired test to determine for each pair of classifiers which one performs better
|
||||
according to each performance metric. When the *p-value* is too high, *ANOVA* power analysis (corrected
|
||||
by *alpha* = $= 0.05$) is performed to determine if the metrics are equally distributed or if the statistical
|
||||
test is inconclusive.
|
||||
|
||||
::: {#tab:precision}
|
||||
| | DecisionTreeClassifier | GaussianNB | MLPClassifier | RandomForestClassifier | SVC |
|
||||
|:-----------------------|:-------------------------|:-------------|:----------------|:-------------------------|------:|
|
||||
| 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|
|
||||
# F1 Values
|
||||
|
||||
: Pairwise Wilcoxon test for precision for each combination of classifiers.
|
||||
:::
|
||||
|
||||
::: {#tab:recall}
|
||||
| | DecisionTreeClassifier | GaussianNB | MLPClassifier | RandomForestClassifier | SVC |
|
||||
|:-----------------------|:-------------------------|:-------------|:----------------|:-------------------------|------:|
|
||||
| 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|
|
||||
: Pairwise Wilcoxon test for recall for each combination of classifiers.
|
||||
:::
|
||||
|
||||
::: {#tab:f1}
|
||||
| | DecisionTreeClassifier | GaussianNB | MLPClassifier | RandomForestClassifier | SVC |
|
||||
|:-----------------------|:-------------------------|:-------------|:----------------|:-------------------------|------:|
|
||||
| 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|
|
||||
: Pairwise Wilcoxon test for the F1 score metric for each combination of classifiers.
|
||||
:::
|
||||
|
||||
### F1 Values
|
||||
|
||||
-
|
||||
|
||||
- \...
|
||||
- Mean *F1* for *DT*: 0.8881, mean *F1* for *NB*: 0.5495 $\Rightarrow$ *DT* is better than *NB* (*p-value* $= 0.0$)
|
||||
- Mean *F1* for *DT*: 0.8881, mean *F1* for *MLP*: 0.8848 $\Rightarrow$ statistical test inconclusive (*p-value* $= 0.4711$, *5% corrected ANOVA power* $= 0.2987$)
|
||||
- Mean *F1* for *DT*: 0.8881, mean *F1* for *RF*: 0.9108 $\Rightarrow$ *RF* is better than *DT* (*p-value* $= 0.0$)
|
||||
- Mean *F1* for *DT*: 0.8881, mean *F1* for *SVP*: 0.7527 $\Rightarrow$ *DT* is better than *SVP* (*p-value* $= 0.0$)
|
||||
- Mean *F1* for *NB*: 0.5495, mean *F1* for *MLP*: 0.8848 $\Rightarrow$ *MLP* is better than *NB* (*p-value* $= 0.0$)
|
||||
- Mean *F1* for *NB*: 0.5495, mean *F1* for *RF*: 0.9108 $\Rightarrow$ *RF* is better than *NB* (*p-value* $= 0.0$)
|
||||
- Mean *F1* for *NB*: 0.5495, mean *F1* for *SVP*: 0.7527 $\Rightarrow$ *SVP* is better than *NB* (*p-value* $= 0.0$)
|
||||
- Mean *F1* for *MLP*: 0.8848, mean *F1* for *RF*: 0.9108 $\Rightarrow$ *RF* is better than *MLP* (*p-value* $= 0.0$)
|
||||
- Mean *F1* for *MLP*: 0.8848, mean *F1* for *SVP*: 0.7527 $\Rightarrow$ *MLP* is better than *SVP* (*p-value* $= 0.0$)
|
||||
- Mean *F1* for *RF*: 0.9108, mean *F1* for *SVP*: 0.7527 $\Rightarrow$ *RF* is better than *SVP* (*p-value* $= 0.0$)
|
||||
- Mean *F1* for *Biased*: 0.6662, mean *F1* for *DT*: 0.8881 $\Rightarrow$ *DT* is better than *Biased* (*p-value* $= 0.0$)
|
||||
- Mean *F1* for *Biased*: 0.6662, mean *F1* for *NB*: 0.5495 $\Rightarrow$ *Biased* is better than *NB* (*p-value* $= 0.0$)
|
||||
- Mean *F1* for *Biased*: 0.6662, mean *F1* for *MLP*: 0.8848 $\Rightarrow$ *MLP* is better than *Biased* (*p-value* $= 0.0$)
|
||||
- Mean *F1* for *Biased*: 0.6662, mean *F1* for *RF*: 0.9108 $\Rightarrow$ *RF* is better than *Biased* (*p-value* $= 0.0$)
|
||||
- Mean *F1* for *Biased*: 0.6662, mean *F1* for *SVP*: 0.7527 $\Rightarrow$ *SVP* is better than *Biased* (*p-value* $= 0.0$)
|
||||
|
||||
### Precision
|
||||
|
||||
(same as for F1 above)
|
||||
- Mean *precision* for *DT*: 0.8327, mean *precision* for *NB*: 0.8209 $\Rightarrow$ *DT* is as effective as *NB* (*p-value* $= 0.0893$, *5% corrected ANOVA power* $= 0.8498$)
|
||||
- Mean *precision* for *DT*: 0.8327, mean *precision* for *MLP*: 0.8365 $\Rightarrow$ statistical test inconclusive (*p-value* $= 0.4012$, *5% corrected ANOVA power* $= 0.2196$)
|
||||
- Mean *precision* for *DT*: 0.8327, mean *precision* for *RF*: 0.8707 $\Rightarrow$ *RF* is better than *DT* (*p-value* $= 0.0$)
|
||||
- Mean *precision* for *DT*: 0.8327, mean *precision* for *SVP*: 0.7557 $\Rightarrow$ *DT* is better than *SVP* (*p-value* $= 0.0$)
|
||||
- Mean *precision* for *NB*: 0.8209, mean *precision* for *MLP*: 0.8365 $\Rightarrow$ *MLP* is better than *NB* (*p-value* $= 0.0348$)
|
||||
- Mean *precision* for *NB*: 0.8209, mean *precision* for *RF*: 0.8707 $\Rightarrow$ *RF* is better than *NB* (*p-value* $= 0.0$)
|
||||
- Mean *precision* for *NB*: 0.8209, mean *precision* for *SVP*: 0.7557 $\Rightarrow$ *NB* is better than *SVP* (*p-value* $= 0.0$)
|
||||
- Mean *precision* for *MLP*: 0.8365, mean *precision* for *RF*: 0.8707 $\Rightarrow$ *RF* is better than *MLP* (*p-value* $= 0.0$)
|
||||
- Mean *precision* for *MLP*: 0.8365, mean *precision* for *SVP*: 0.7557 $\Rightarrow$ *MLP* is better than *SVP* (*p-value* $= 0.0$)
|
||||
- Mean *precision* for *RF*: 0.8707, mean *precision* for *SVP*: 0.7557 $\Rightarrow$ *RF* is better than *SVP* (*p-value* $= 0.0$)
|
||||
- Mean *precision* for *Biased*: 0.4995, mean *precision* for *DT*: 0.8327 $\Rightarrow$ *DT* is better than *Biased* (*p-value* $= 0.0$)
|
||||
- Mean *precision* for *Biased*: 0.4995, mean *precision* for *NB*: 0.8209 $\Rightarrow$ *NB* is better than *Biased* (*p-value* $= 0.0$)
|
||||
- Mean *precision* for *Biased*: 0.4995, mean *precision* for *MLP*: 0.8365 $\Rightarrow$ *MLP* is better than *Biased* (*p-value* $= 0.0$)
|
||||
- Mean *precision* for *Biased*: 0.4995, mean *precision* for *RF*: 0.8707 $\Rightarrow$ *RF* is better than *Biased* (*p-value* $= 0.0$)
|
||||
- Mean *precision* for *Biased*: 0.4995, mean *precision* for *SVP*: 0.7557 $\Rightarrow$ *SVP* is better than *Biased* (*p-value* $= 0.0$)
|
||||
|
||||
### Recall
|
||||
|
||||
(same as for F1 above)
|
||||
- Mean *recall* for *DT*: 0.9533, mean *recall* for *NB*: 0.4189 $\Rightarrow$ *DT* is better than *NB* (*p-value* $= 0.0$)
|
||||
- Mean *recall* for *DT*: 0.9533, mean *recall* for *MLP*: 0.9418 $\Rightarrow$ *DT* is better than *MLP* (*p-value* $= 0.0118$)
|
||||
- Mean *recall* for *DT*: 0.9533, mean *recall* for *RF*: 0.9567 $\Rightarrow$ statistical test inconclusive (*p-value* $= 0.3276$, *5% corrected ANOVA power* $= 0.2558$)
|
||||
- Mean *recall* for *DT*: 0.9533, mean *recall* for *SVP*: 0.7547 $\Rightarrow$ *DT* is better than *SVP* (*p-value* $= 0.0$)
|
||||
- Mean *recall* for *NB*: 0.4189, mean *recall* for *MLP*: 0.9418 $\Rightarrow$ *MLP* is better than *NB* (*p-value* $= 0.0$)
|
||||
- Mean *recall* for *NB*: 0.4189, mean *recall* for *RF*: 0.9567 $\Rightarrow$ *RF* is better than *NB* (*p-value* $= 0.0$)
|
||||
- Mean *recall* for *NB*: 0.4189, mean *recall* for *SVP*: 0.7547 $\Rightarrow$ *SVP* is better than *NB* (*p-value* $= 0.0$)
|
||||
- Mean *recall* for *MLP*: 0.9418, mean *recall* for *RF*: 0.9567 $\Rightarrow$ *RF* is better than *MLP* (*p-value* $= 0.0001$)
|
||||
- Mean *recall* for *MLP*: 0.9418, mean *recall* for *SVP*: 0.7547 $\Rightarrow$ *MLP* is better than *SVP* (*p-value* $= 0.0$)
|
||||
- Mean *recall* for *RF*: 0.9567, mean *recall* for *SVP*: 0.7547 $\Rightarrow$ *RF* is better than *SVP* (*p-value* $= 0.0$)
|
||||
- Mean *recall* for *Biased*: 1.0, mean *recall* for *DT*: 0.9533 $\Rightarrow$ *Biased* is better than *DT* (*p-value* $= 0.0$)
|
||||
- Mean *recall* for *Biased*: 1.0, mean *recall* for *NB*: 0.4189 $\Rightarrow$ *Biased* is better than *NB* (*p-value* $= 0.0$)
|
||||
- Mean *recall* for *Biased*: 1.0, mean *recall* for *MLP*: 0.9418 $\Rightarrow$ *Biased* is better than *MLP* (*p-value* $= 0.0$)
|
||||
- Mean *recall* for *Biased*: 1.0, mean *recall* for *RF*: 0.9567 $\Rightarrow$ *Biased* is better than *RF* (*p-value* $= 0.0$)
|
||||
- Mean *recall* for *Biased*: 1.0, mean *recall* for *SVP*: 0.7547 $\Rightarrow$ *Biased* is better than *SVP* (*p-value* $= 0.0$)
|
||||
|
||||
## Practical Usefulness
|
||||
|
||||
|
|
|
|||
BIN
report/main.pdf
BIN
report/main.pdf
Binary file not shown.
|
|
@ -3,4 +3,5 @@ pandas==1.5.2
|
|||
scikit_learn==1.2.1
|
||||
tabulate==0.9.0
|
||||
scipy==1.24.2
|
||||
seaborn==0.12.2
|
||||
seaborn==0.12.2
|
||||
statsmodels==0.14.0
|
||||
Reference in a new issue