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output_100_25000.txt
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output_100_25000.txt
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λ python .\dogs_vs_cats.py
Nr dogs: 12500
Nr cats: 12500
Nearest Neighbors
Score: 0.6524
Confusion matrix:
[[1808 1942]
[ 665 3085]]
Classification report:
precision recall f1-score support
cat 0.73 0.48 0.58 3750
dog 0.61 0.82 0.70 3750
avg / total 0.67 0.65 0.64 7500
Gaussian Naive Bayes
Score: 0.624133333333
Confusion matrix:
[[2151 1599]
[1220 2530]]
Classification report:
precision recall f1-score support
cat 0.64 0.57 0.60 3750
dog 0.61 0.67 0.64 3750
avg / total 0.63 0.62 0.62 7500
SVM
Score: 0.689866666667
Confusion matrix:
[[2745 1005]
[1321 2429]]
Classification report:
precision recall f1-score support
cat 0.68 0.73 0.70 3750
dog 0.71 0.65 0.68 3750
avg / total 0.69 0.69 0.69 7500
AdaBoos
Score: 0.697066666667
Confusion matrix:
[[2688 1062]
[1210 2540]]
Classification report:
precision recall f1-score support
cat 0.69 0.72 0.70 3750
dog 0.71 0.68 0.69 3750
avg / total 0.70 0.70 0.70 7500
Random Forest
Score: 0.724
Confusion matrix:
[[2963 787]
[1283 2467]]
Classification report:
precision recall f1-score support
cat 0.70 0.79 0.74 3750
dog 0.76 0.66 0.70 3750
avg / total 0.73 0.72 0.72 7500
Classifier: RandomForestClassifier
Score: 0.724
Confusion matrix:
[[2963 787]
[1283 2467]]
Classification report:
precision recall f1-score support
cat 0.70 0.79 0.74 3750
dog 0.76 0.66 0.70 3750
avg / total 0.73 0.72 0.72 7500
Traceback (most recent call last):
File ".\dogs_vs_cats.py", line 436, in <module>
p = best_clf.predict(img_features)
File "C:\Anaconda3\envs\VCOM\lib\site-packages\sklearn\ensemble\forest.py", line 498, in predict
proba = self.predict_proba(X)
File "C:\Anaconda3\envs\VCOM\lib\site-packages\sklearn\ensemble\forest.py", line 537, in predict_proba
X = self._validate_X_predict(X)
File "C:\Anaconda3\envs\VCOM\lib\site-packages\sklearn\ensemble\forest.py", line 319, in _validate_X_predict
return self.estimators_[0]._validate_X_predict(X, check_input=True)
File "C:\Anaconda3\envs\VCOM\lib\site-packages\sklearn\tree\tree.py", line 365, in _validate_X_predict
X = check_array(X, dtype=DTYPE, accept_sparse="csr")
File "C:\Anaconda3\envs\VCOM\lib\site-packages\sklearn\utils\validation.py", line 398, in check_array
_assert_all_finite(array)
File "C:\Anaconda3\envs\VCOM\lib\site-packages\sklearn\utils\validation.py", line 54, in _assert_all_finite
" or a value too large for %r." % X.dtype)
ValueError: Input contains NaN, infinity or a value too large for dtype('float32').