machine-learningunsupervised-learninganomaly-detectionisolation-forest
How to know which features causes anomalies while training isolation forest model
I'm training an unsupersived isolation forest model with a dataframe that contains 10 features , the model performs well and detect anomalies. My question is if an anomaly is catched i want to know which feature(s) has caused that anomaly. Is there any way to do it ? If not , is there an other model that allows me to do it
Solution
SHAP values and the shap library can be used for this. See this answer for an example.
After getting the shap values out of the explainer for your datapoints, you can use the waterfall plots to see how different features contributed to the decision.
shap.plots.waterfall(shap_values[0])
It will give a plot similar to this:
- classifiers in scikit-learn that handle nan/null
- Encoding two categorial data present in same dataset in Deep learning
- Machine learning in q/kdb+
- Why do the sensitivity (recall) values differ between classification_report and precision_recall_fscore_support in a loop?
- How do you specify the bfloat16 mixed precision with the Intel Extension for PyTorch?
- mlflow doesn't autolog artifacts while logging images
- Choosing top k models using GridSearchCV in scikit-learn
- Calculating pairwise distances between entries in a `torch.tensor`
- How to fix the learning-rate for Huggingface´s Trainer?
- Reusing a feature to split regression decision tree's nodes
- Fine tuning LayoutLmv3 using Cord-V2 dataset
- What does model.eval() do in pytorch?
- ElasticNetCV in Python: Get full grid of hyperparameters with corresponding MSE?
- Why is the accuracy for my Keras model always 0 when training?
- Polars - issues with performance - attempting to create a new dataframe per row
- Keras model.export() fails because of NoneType shapes in model
- Meta-feature analysis: split data for computation on available memory
- Single-headed transformer model, output and target tensor sizes don't match
- How to add the bounding box values to the labels text files during prediction with a trained YOLO-V8 instance segmentation model?
- How to calculate correlation of colours in a dataset?
- Convert array of indices to one-hot encoded array in NumPy
- Import custom prediction routine to vertex AI pipeline
- what is the difference between bigram and unigram text features extraction
- How to map the results of Principal Component Analysis back to the actual features that were fed into the model?
- How to calculate optimal batch size?
- Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample
- Center an image and adding a background at export
- Control where Source Code for Azure ML Command gets Uploaded
- sklearn ImportError: cannot import name plot_roc_curve
- How to remove words from a sentence that carry no positive or negative sentiment?