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Commit 5b81d9fc authored by Ard Kastrati's avatar Ard Kastrati
Browse files

merging conflicts

parents 4c570f13 e0373b66
......@@ -27,24 +27,24 @@ from config import config
def cross_validate_kNN(X, y):
logging.info("Cross-validation KNN...")
classifier = KNeighborsClassifier(weights='uniform', algorithm='auto', n_jobs=-1)
parameters_KNN = {'n_neighbors': [5, 10, 50], 'leaf_size': [10, 30]}
parameters_KNN = {'n_neighbors': [5, 25, 100], 'leaf_size': [10, 50]}
cross_validate(classifier=classifier, parameters=parameters_KNN, X=X, y=y)
def cross_validate_SVC(X, y):
logging.info("Cross-validation SVC...")
classifier = LinearSVC(tol=1e-5)
parameters_SVC = {'C': [1, 10, 100, 1000, 10000], 'max_iter': [1000, 10000, 100000]}
classifier = LinearSVC(tol=1e-2, max_iter=500)
parameters_SVC = {'C': [0.001, 0.01, 0.1]}
cross_validate(classifier=classifier, parameters=parameters_SVC, X=X, y=y)
def cross_validate_RFC(X, y):
logging.info("Cross-validation RFC...")
classifier = RandomForestClassifier(max_features='auto', random_state=42, n_jobs=-1)
parameters_RFC = {'n_estimators': [10, 50, 100, 1000], 'max_depth': [5, 10, 50, 100, 500], 'min_samples_split' : [0.1, 0.4, 0.7, 1.0], 'min_samples_leaf': [0.1, 0.5]}
parameters_RFC = {'n_estimators': [10, 50, 100, 1000], 'max_depth': [5, 10, 50, 100, 500], 'min_samples_split' : [0.1, 0.4, 0.7, 1.0], 'min_samples_leaf' : [0.1, 0.5]}
cross_validate(classifier=classifier, parameters=parameters_RFC, X=X, y=y)
def cross_validate(classifier, parameters, X, y):
X = X.reshape((100, 500 * 129))
clf = GridSearchCV(classifier, parameters, scoring='accuracy', n_jobs=-1, verbose=3, cv=2)
X = X.reshape((36223, 500 * 129))
clf = GridSearchCV(classifier, parameters, scoring='accuracy', verbose=3, cv=2)
clf.fit(X, y.ravel())
export_dict(clf.cv_results_['mean_fit_time'], clf.cv_results_['std_fit_time'], clf.cv_results_['mean_score_time'],
......@@ -59,19 +59,21 @@ def cross_validate(classifier, parameters, X, y):
def try_sklearn_classifiers(X, y):
logging.info("Training the simple classifiers: kNN, Linear SVM, Random Forest and Naive Bayes.")
names = [# "Nearest Neighbors",
#"Linear SVM",
"Random Forest",
"Naive Bayes",
# "Linear SVM",
# "Random Forest",
# "Naive Bayes",
"Linear SVM"
]
classifiers = [
# KNeighborsClassifier(n_neighbors=5, weights='uniform', algorithm='auto', leaf_size=30, n_jobs=-1),
LinearSVC(tol=1e-5, C=1, random_state=42, max_iter=1000, verbose=1),
RandomForestClassifier(n_estimators=100, max_depth=5, max_features='auto', random_state=42, n_jobs=-1, verbose=1),
GaussianNB()
# LinearSVC(tol=1e-5, C=1, random_state=42, max_iter=1000),
# RandomForestClassifier(n_estimators=30, max_depth=20, max_features='auto', random_state=42, n_jobs=-1),
# GaussianNB(),
LinearSVC(tol=1e-3, C=0.1, random_state=42, max_iter=12000)
]
X = X.reshape((100, 500 * 129))
X = X.reshape((36223, 500 * 129))
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2, random_state=42)
scores = []
......@@ -98,4 +100,4 @@ def export_dict(*columns, first_row, file_name):
writer = csv.writer(f)
writer.writerow(first_row)
for row in rows:
writer.writerow(row)
\ No newline at end of file
writer.writerow(row)
......@@ -51,7 +51,7 @@ def main():
# try_sklearn_classifiers(trainX, trainY)
# cross_validate_kNN(trainX, trainY)
# cross_validate_SVC(trainX, trainY)
cross_validate_RFC(trainX, trainY)
# cross_validate_RFC(trainX, trainY)
else:
# tune(trainX,trainY)
run(trainX,trainY)
......
BEST ESTIMATOR,BEST SCORE,BEST PARAMS
"RandomForestClassifier(max_depth=5, min_samples_leaf=0.1, min_samples_split=0.1,
n_estimators=1000, n_jobs=-1, random_state=42)",0.6598846369794981,"{'max_depth': 5, 'min_samples_leaf': 0.1, 'min_samples_split': 0.1, 'n_estimators': 1000}"
(36223, 500, 129)
(36223, 1)
Fitting 2 folds for each of 160 candidates, totalling 320 fits
INFO:root:Started the Logging
INFO:root:X training loaded.
INFO:root:(129, 500, 36223)
INFO:root:y training loaded.
INFO:root:(1, 36223)
INFO:root:Setting the shapes
INFO:root:(36223, 500, 129)
INFO:root:(36223, 1)
INFO:root:Cross-validation RFC...
INFO:root:--- Runtime: 6324.672133684158 seconds ---
INFO:root:Finished Logging
This diff is collapsed.
(36223, 500, 129)
(36223, 1)
Fitting 2 folds for each of 3 candidates, totalling 6 fits
[CV] C=0.001 .........................................................
[CV] ............................. C=0.001, score=0.699, total=18.4min
[CV] C=0.001 .........................................................
[CV] ............................. C=0.001, score=0.697, total=17.6min
[CV] C=0.01 ..........................................................
[CV] .............................. C=0.01, score=0.699, total=17.3min
[CV] C=0.01 ..........................................................
[CV] .............................. C=0.01, score=0.697, total=17.0min
[CV] C=0.1 ...........................................................
[CV] ............................... C=0.1, score=0.699, total=16.0min
[CV] C=0.1 ...........................................................
[CV] ............................... C=0.1, score=0.697, total=16.2min
INFO:root:Started the Logging
INFO:root:X training loaded.
INFO:root:(129, 500, 36223)
INFO:root:y training loaded.
INFO:root:(1, 36223)
INFO:root:Setting the shapes
INFO:root:(36223, 500, 129)
INFO:root:(36223, 1)
INFO:root:Cross-validation SVC...
Sender: LSF System <lsfadmin@eu-g1-042-3>
Subject: Job 166917611: <python /cluster/home/mplomecka/dl-project/main.py> in cluster <euler> Exited
Job <python /cluster/home/mplomecka/dl-project/main.py> was submitted from host <eu-login-16> by user <mplomecka> in cluster <euler> at Wed Mar 24 12:39:56 2021
Job was executed on host(s) <15*eu-g1-042-3>, in queue <bigmem.24h>, as user <mplomecka> in cluster <euler> at Wed Mar 24 12:40:24 2021
</cluster/home/mplomecka> was used as the home directory.
</cluster/home/mplomecka/dl-project> was used as the working directory.
Started at Wed Mar 24 12:40:24 2021
Terminated at Wed Mar 24 14:26:21 2021
Results reported at Wed Mar 24 14:26:21 2021
Your job looked like:
------------------------------------------------------------
# LSBATCH: User input
python /cluster/home/mplomecka/dl-project/main.py
------------------------------------------------------------
Exited with exit code 1.
Resource usage summary:
CPU time : 6339.34 sec.
Max Memory : 83525 MB
Average Memory : 76098.60 MB
Total Requested Memory : 120000.00 MB
Delta Memory : 36475.00 MB
Max Swap : 2884 MB
Max Processes : 3
Max Threads : 4
Run time : 6376 sec.
Turnaround time : 6385 sec.
The output (if any) follows:
2021-03-24 12:40:25.992268: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
(36223, 500, 129)
(36223, 1)
Fitting 2 folds for each of 3 candidates, totalling 6 fits
[CV] C=0.001 .........................................................
[CV] ............................. C=0.001, score=0.699, total=18.4min
[CV] C=0.001 .........................................................
[CV] ............................. C=0.001, score=0.697, total=17.6min
[CV] C=0.01 ..........................................................
[CV] .............................. C=0.01, score=0.699, total=17.3min
[CV] C=0.01 ..........................................................
[CV] .............................. C=0.01, score=0.697, total=17.0min
[CV] C=0.1 ...........................................................
[CV] ............................... C=0.1, score=0.699, total=16.0min
[CV] C=0.1 ...........................................................
[CV] ............................... C=0.1, score=0.697, total=16.2min
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
/cluster/apps/nss/gcc-6.3.0/python/3.8.5/x86_64/lib64/python3.8/site-packages/sklearn/svm/_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn("Liblinear failed to converge, increase "
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 18.4min remaining: 0.0s
/cluster/apps/nss/gcc-6.3.0/python/3.8.5/x86_64/lib64/python3.8/site-packages/sklearn/svm/_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn("Liblinear failed to converge, increase "
[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 36.0min remaining: 0.0s
/cluster/apps/nss/gcc-6.3.0/python/3.8.5/x86_64/lib64/python3.8/site-packages/sklearn/svm/_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn("Liblinear failed to converge, increase "
/cluster/apps/nss/gcc-6.3.0/python/3.8.5/x86_64/lib64/python3.8/site-packages/sklearn/svm/_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn("Liblinear failed to converge, increase "
/cluster/apps/nss/gcc-6.3.0/python/3.8.5/x86_64/lib64/python3.8/site-packages/sklearn/svm/_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn("Liblinear failed to converge, increase "
/cluster/apps/nss/gcc-6.3.0/python/3.8.5/x86_64/lib64/python3.8/site-packages/sklearn/svm/_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn("Liblinear failed to converge, increase "
[Parallel(n_jobs=1)]: Done 6 out of 6 | elapsed: 102.5min finished
Traceback (most recent call last):
File "/cluster/home/mplomecka/dl-project/main.py", line 65, in <module>
main()
File "/cluster/home/mplomecka/dl-project/main.py", line 53, in main
cross_validate_SVC(trainX, trainY)
File "/cluster/home/mplomecka/dl-project/SimpleClassifiers/sklearnclassifier.py", line 37, in cross_validate_SVC
cross_validate(classifier=classifier, parameters=parameters_SVC, X=X, y=y)
File "/cluster/home/mplomecka/dl-project/SimpleClassifiers/sklearnclassifier.py", line 48, in cross_validate
clf.fit(X, y.ravel())
File "/cluster/apps/nss/gcc-6.3.0/python/3.8.5/x86_64/lib64/python3.8/site-packages/sklearn/utils/validation.py", line 72, in inner_f
return f(**kwargs)
File "/cluster/apps/nss/gcc-6.3.0/python/3.8.5/x86_64/lib64/python3.8/site-packages/sklearn/model_selection/_search.py", line 765, in fit
self.best_estimator_.fit(X, y, **fit_params)
File "/cluster/apps/nss/gcc-6.3.0/python/3.8.5/x86_64/lib64/python3.8/site-packages/sklearn/svm/_classes.py", line 233, in fit
self.coef_, self.intercept_, self.n_iter_ = _fit_liblinear(
File "/cluster/apps/nss/gcc-6.3.0/python/3.8.5/x86_64/lib64/python3.8/site-packages/sklearn/svm/_base.py", line 966, in _fit_liblinear
raw_coef_, n_iter_ = liblinear.train_wrap(
File "sklearn/svm/_liblinear.pyx", line 39, in sklearn.svm._liblinear.train_wrap
OverflowError: value too large to convert to npy_int32
(36223, 500, 129)
(36223, 1)
Fitting 2 folds for each of 5 candidates, totalling 10 fits
[CV] C=1 .............................................................
[CV] ................................. C=1, score=0.699, total=16.2min
[CV] C=1 .............................................................
[CV] ................................. C=1, score=0.697, total=16.6min
[CV] C=10 ............................................................
[CV] ................................ C=10, score=0.699, total=16.5min
[CV] C=10 ............................................................
[CV] ................................ C=10, score=0.697, total=16.1min
[CV] C=100 ...........................................................
[CV] ............................... C=100, score=0.699, total=14.7min
[CV] C=100 ...........................................................
[CV] ............................... C=100, score=0.697, total=14.4min
[CV] C=1000 ..........................................................
[CV] .............................. C=1000, score=0.699, total=14.8min
[CV] C=1000 ..........................................................
[CV] .............................. C=1000, score=0.697, total=14.8min
[CV] C=10000 .........................................................
[CV] ............................. C=10000, score=0.699, total=15.8min
[CV] C=10000 .........................................................
[CV] ............................. C=10000, score=0.697, total=15.4min
INFO:root:Started the Logging
INFO:root:X training loaded.
INFO:root:(129, 500, 36223)
INFO:root:y training loaded.
INFO:root:(1, 36223)
INFO:root:Setting the shapes
INFO:root:(36223, 500, 129)
INFO:root:(36223, 1)
INFO:root:Cross-validation SVC...
Sender: LSF System <lsfadmin@eu-g1-020-2>
Subject: Job 166889268: <python /cluster/home/mplomecka/dl-project/main.py> in cluster <euler> Exited
Job <python /cluster/home/mplomecka/dl-project/main.py> was submitted from host <eu-login-16> by user <mplomecka> in cluster <euler> at Wed Mar 24 09:23:07 2021
Job was executed on host(s) <15*eu-g1-020-2>, in queue <bigmem.24h>, as user <mplomecka> in cluster <euler> at Wed Mar 24 09:23:13 2021
</cluster/home/mplomecka> was used as the home directory.
</cluster/home/mplomecka/dl-project> was used as the working directory.
Started at Wed Mar 24 09:23:13 2021
Terminated at Wed Mar 24 12:01:46 2021
Results reported at Wed Mar 24 12:01:46 2021
Your job looked like:
------------------------------------------------------------
# LSBATCH: User input
python /cluster/home/mplomecka/dl-project/main.py
------------------------------------------------------------
Exited with exit code 1.
Resource usage summary:
CPU time : 9496.02 sec.
Max Memory : 85092 MB
Average Memory : 83600.53 MB
Total Requested Memory : 120000.00 MB
Delta Memory : 34908.00 MB
Max Swap : -
Max Processes : 3
Max Threads : 4
Run time : 9526 sec.
Turnaround time : 9519 sec.
The output (if any) follows:
2021-03-24 09:23:16.263690: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
(36223, 500, 129)
(36223, 1)
Fitting 2 folds for each of 5 candidates, totalling 10 fits
[CV] C=1 .............................................................
[CV] ................................. C=1, score=0.699, total=16.2min
[CV] C=1 .............................................................
[CV] ................................. C=1, score=0.697, total=16.6min
[CV] C=10 ............................................................
[CV] ................................ C=10, score=0.699, total=16.5min
[CV] C=10 ............................................................
[CV] ................................ C=10, score=0.697, total=16.1min
[CV] C=100 ...........................................................
[CV] ............................... C=100, score=0.699, total=14.7min
[CV] C=100 ...........................................................
[CV] ............................... C=100, score=0.697, total=14.4min
[CV] C=1000 ..........................................................
[CV] .............................. C=1000, score=0.699, total=14.8min
[CV] C=1000 ..........................................................
[CV] .............................. C=1000, score=0.697, total=14.8min
[CV] C=10000 .........................................................
[CV] ............................. C=10000, score=0.699, total=15.8min
[CV] C=10000 .........................................................
[CV] ............................. C=10000, score=0.697, total=15.4min
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
/cluster/apps/nss/gcc-6.3.0/python/3.8.5/x86_64/lib64/python3.8/site-packages/sklearn/svm/_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn("Liblinear failed to converge, increase "
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 16.2min remaining: 0.0s
/cluster/apps/nss/gcc-6.3.0/python/3.8.5/x86_64/lib64/python3.8/site-packages/sklearn/svm/_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn("Liblinear failed to converge, increase "
[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 32.8min remaining: 0.0s
/cluster/apps/nss/gcc-6.3.0/python/3.8.5/x86_64/lib64/python3.8/site-packages/sklearn/svm/_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn("Liblinear failed to converge, increase "
/cluster/apps/nss/gcc-6.3.0/python/3.8.5/x86_64/lib64/python3.8/site-packages/sklearn/svm/_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn("Liblinear failed to converge, increase "
/cluster/apps/nss/gcc-6.3.0/python/3.8.5/x86_64/lib64/python3.8/site-packages/sklearn/svm/_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn("Liblinear failed to converge, increase "
/cluster/apps/nss/gcc-6.3.0/python/3.8.5/x86_64/lib64/python3.8/site-packages/sklearn/svm/_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn("Liblinear failed to converge, increase "
/cluster/apps/nss/gcc-6.3.0/python/3.8.5/x86_64/lib64/python3.8/site-packages/sklearn/svm/_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn("Liblinear failed to converge, increase "
/cluster/apps/nss/gcc-6.3.0/python/3.8.5/x86_64/lib64/python3.8/site-packages/sklearn/svm/_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn("Liblinear failed to converge, increase "
/cluster/apps/nss/gcc-6.3.0/python/3.8.5/x86_64/lib64/python3.8/site-packages/sklearn/svm/_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn("Liblinear failed to converge, increase "
/cluster/apps/nss/gcc-6.3.0/python/3.8.5/x86_64/lib64/python3.8/site-packages/sklearn/svm/_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn("Liblinear failed to converge, increase "
[Parallel(n_jobs=1)]: Done 10 out of 10 | elapsed: 155.5min finished
Traceback (most recent call last):
File "/cluster/home/mplomecka/dl-project/main.py", line 65, in <module>
main()
File "/cluster/home/mplomecka/dl-project/main.py", line 53, in main
cross_validate_SVC(trainX, trainY)
File "/cluster/home/mplomecka/dl-project/SimpleClassifiers/sklearnclassifier.py", line 37, in cross_validate_SVC
cross_validate(classifier=classifier, parameters=parameters_SVC, X=X, y=y)
File "/cluster/home/mplomecka/dl-project/SimpleClassifiers/sklearnclassifier.py", line 48, in cross_validate
clf.fit(X, y.ravel())
File "/cluster/apps/nss/gcc-6.3.0/python/3.8.5/x86_64/lib64/python3.8/site-packages/sklearn/utils/validation.py", line 72, in inner_f
return f(**kwargs)
File "/cluster/apps/nss/gcc-6.3.0/python/3.8.5/x86_64/lib64/python3.8/site-packages/sklearn/model_selection/_search.py", line 765, in fit
self.best_estimator_.fit(X, y, **fit_params)
File "/cluster/apps/nss/gcc-6.3.0/python/3.8.5/x86_64/lib64/python3.8/site-packages/sklearn/svm/_classes.py", line 233, in fit
self.coef_, self.intercept_, self.n_iter_ = _fit_liblinear(
File "/cluster/apps/nss/gcc-6.3.0/python/3.8.5/x86_64/lib64/python3.8/site-packages/sklearn/svm/_base.py", line 966, in _fit_liblinear
raw_coef_, n_iter_ = liblinear.train_wrap(
File "sklearn/svm/_liblinear.pyx", line 39, in sklearn.svm._liblinear.train_wrap
OverflowError: value too large to convert to npy_int32
Model,Score,Runtime
Random Forest,0.6449965493443754,18.374440908432007
Naive Bayes,0.5421670117322291,54.8563334941864
Linear SVM,0.7054520358868185,2287.276668548584
RandomForestClassifier(n_estimators=30, max_depth=20, max_features='auto', random_state=42, n_jobs=-1),
GaussianNB(),
LinearSVC(tol=1e-3, C=5, random_state=42, max_iter=500)
INFO:root:Started the Logging
INFO:root:X training loaded.
INFO:root:(129, 500, 36223)
INFO:root:y training loaded.
INFO:root:(1, 36223)
INFO:root:Setting the shapes
INFO:root:(36223, 500, 129)
INFO:root:(36223, 1)
INFO:root:Training the simple classifiers: kNN, Linear SVM, Random Forest and Naive Bayes.
INFO:root:Random Forest
INFO:root:--- Score: 0.6449965493443754
INFO:root:--- Runtime: 18.374440908432007 for seconds ---
INFO:root:Naive Bayes
INFO:root:--- Score: 0.5421670117322291
INFO:root:--- Runtime: 54.8563334941864 for seconds ---
INFO:root:Linear SVM
INFO:root:--- Score: 0.7054520358868185
INFO:root:--- Runtime: 2287.276668548584 for seconds ---
INFO:root:--- Runtime: 2564.1652948856354 seconds ---
INFO:root:Finished Logging
Sender: LSF System <lsfadmin@eu-a6-008-18>
Subject: Job 166812903: <python /cluster/home/kard/dl-project/main.py> in cluster <euler> Done
Job <python /cluster/home/kard/dl-project/main.py> was submitted from host <eu-login-28> by user <kard> in cluster <euler> at Tue Mar 23 21:37:20 2021
Job was executed on host(s) <15*eu-a6-008-18>, in queue <bigmem.24h>, as user <kard> in cluster <euler> at Tue Mar 23 21:40:02 2021
</cluster/home/kard> was used as the home directory.
</cluster/home/kard/dl-project> was used as the working directory.
Started at Tue Mar 23 21:40:02 2021
Terminated at Tue Mar 23 22:22:58 2021
Results reported at Tue Mar 23 22:22:58 2021
Your job looked like:
------------------------------------------------------------
# LSBATCH: User input
python /cluster/home/kard/dl-project/main.py
------------------------------------------------------------
Successfully completed.
Resource usage summary:
CPU time : 2859.81 sec.
Max Memory : 95877 MB
Average Memory : 89903.56 MB
Total Requested Memory : 120000.00 MB
Delta Memory : 24123.00 MB
Max Swap : -
Max Processes : 3
Max Threads : 37
Run time : 2576 sec.
Turnaround time : 2738 sec.
The output (if any) follows:
2021-03-23 21:40:04.922001: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
(36223, 500, 129)
(36223, 1)
/cluster/apps/nss/gcc-6.3.0/python/3.8.5/x86_64/lib64/python3.8/site-packages/sklearn/svm/_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn("Liblinear failed to converge, increase "
Model,Score,Runtime
Nearest Neighbors,0.5412008281573499,3658.430173397064
Linear SVM,0.7055900621118012,30067.27211046219
Random Forest,0.6946859903381642,1354.794748544693
Naive Bayes,0.5421670117322291,57.323140382766724
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