Isolation Forest Prediction
$begingroup$
To compute anomaly score we use whole dataset to test or only test dataset? Can someone please help.
And with streaming data we doesn't train so in that we are using whole dataset to find accuracy. So how would we compare both without streaming and with streaming.
Thank you.
python
asked 8 hours ago
ShivanyaShivanya
164
$endgroup$
add a comment |
$begingroup$
To compute anomaly score we use whole dataset to test or only test dataset? Can someone please help.
And with streaming data we doesn't train so in that we are using whole dataset to find accuracy. So how would we compare both without streaming and with streaming.
Thank you.
python
asked 8 hours ago
ShivanyaShivanya
164
$endgroup$
add a comment |
$begingroup$
To compute anomaly score we use whole dataset to test or only test dataset? Can someone please help.
And with streaming data we doesn't train so in that we are using whole dataset to find accuracy. So how would we compare both without streaming and with streaming.
Thank you.
python
asked 8 hours ago
ShivanyaShivanya
164
$endgroup$
To compute anomaly score we use whole dataset to test or only test dataset? Can someone please help.
And with streaming data we doesn't train so in that we are using whole dataset to find accuracy. So how would we compare both without streaming and with streaming.
Thank you.
python
python
asked 8 hours ago
ShivanyaShivanya
164
asked 8 hours ago
ShivanyaShivanya
164
asked 8 hours ago
ShivanyaShivanya
164
asked 8 hours ago
ShivanyaShivanya
164
asked 8 hours ago
ShivanyaShivanya
164
164
add a comment |
add a comment |
1 Answer
1
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votes
$begingroup$
The training dataset is only used to fit/train the model. The training will extract information from your training data and store it in your model so after training you no longer need it. You should never include training data when you later check the performance/accuracy of your model, that is only the test set.
For predictions and computation of anomaly scores you only need to use the dataset you actually want to check for anomalies/outliers.
For streaming you should train first on offline data which only contains inliers. Once it is trained you can use it to make predictions on new streamed data without comparing to your offline data.
answered 7 hours ago
Simon LarssonSimon Larsson
687114
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add a comment |
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1 Answer
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active
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1 Answer
1
active
oldest
votes
active
oldest
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active
oldest
votes
$begingroup$
The training dataset is only used to fit/train the model. The training will extract information from your training data and store it in your model so after training you no longer need it. You should never include training data when you later check the performance/accuracy of your model, that is only the test set.
For predictions and computation of anomaly scores you only need to use the dataset you actually want to check for anomalies/outliers.
For streaming you should train first on offline data which only contains inliers. Once it is trained you can use it to make predictions on new streamed data without comparing to your offline data.
answered 7 hours ago
Simon LarssonSimon Larsson
687114
$endgroup$
add a comment |
$begingroup$
The training dataset is only used to fit/train the model. The training will extract information from your training data and store it in your model so after training you no longer need it. You should never include training data when you later check the performance/accuracy of your model, that is only the test set.
For predictions and computation of anomaly scores you only need to use the dataset you actually want to check for anomalies/outliers.
For streaming you should train first on offline data which only contains inliers. Once it is trained you can use it to make predictions on new streamed data without comparing to your offline data.
answered 7 hours ago
Simon LarssonSimon Larsson
687114
$endgroup$
add a comment |
$begingroup$
The training dataset is only used to fit/train the model. The training will extract information from your training data and store it in your model so after training you no longer need it. You should never include training data when you later check the performance/accuracy of your model, that is only the test set.
For predictions and computation of anomaly scores you only need to use the dataset you actually want to check for anomalies/outliers.
For streaming you should train first on offline data which only contains inliers. Once it is trained you can use it to make predictions on new streamed data without comparing to your offline data.
answered 7 hours ago
Simon LarssonSimon Larsson
687114
$endgroup$
The training dataset is only used to fit/train the model. The training will extract information from your training data and store it in your model so after training you no longer need it. You should never include training data when you later check the performance/accuracy of your model, that is only the test set.
For predictions and computation of anomaly scores you only need to use the dataset you actually want to check for anomalies/outliers.
For streaming you should train first on offline data which only contains inliers. Once it is trained you can use it to make predictions on new streamed data without comparing to your offline data.
answered 7 hours ago
Simon LarssonSimon Larsson
687114
answered 7 hours ago
Simon LarssonSimon Larsson
687114
answered 7 hours ago
Simon LarssonSimon Larsson
687114
answered 7 hours ago
Simon LarssonSimon Larsson
687114
687114
add a comment |
add a comment |
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