MariaP
Reputation: 1
Precision-Recall curve for LSTM Autoencoder results
I am using an LSTM autoencoder to detect anomalies in a time series. I use the reconstruction error to check if a point is anomalous or not. However, my question is: can I use the precision_recall_curve from Python and give as arguments the truth values with 0 and 1 and the MSE that my model provides? Will I get precision/recall and thresholds related to the MSE in this case? I am confused since I don’t have a regular classifier that outputs probabilities.
Upvotes: 0
Views: 14
Answers (0)
Related Questions
- Keras LSTM Autoencoder time-series reconstruction
- LSTM Autoencoder on timeseries
- LSTM/GRU autoencoder convergency
- Sklearn precision recall curve pos_label for unbalanced dataset which class probability to use
- Questions about feature selection and data engineering when using H2O autoencoder for anomaly detection
- Understand the output of LSTM autoencoder and use it to detect outliers in a sequence
- Variational Autoencoder on Timeseries with LSTM in Keras
- sklearn.metrics.precision_recall_curve: Why are the precision and recall returned arrays instead of single values