Hi, my dataset consists of categorical values and I’ve label encoded them to use isolation forest model. But how to evaluate my model? What metrics should I follow?
If your 'features' are categorical, don't label encode then. Label encoding is meant for Target variables. Evaluating models can be done as you would with any other predictive model
Very nice explanation.
One of the best explanations out here! Thankyou
Very nice explanation. Thanks for the help!
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Very good explanation.
Suppose for single set a feature values, I am getting as outlier. How to find out top features contributed for the decision?
Nice ...Thanks
helped me a lot, keep up he good work!
Thanks 🙏
Hi, my dataset consists of categorical values and I’ve label encoded them to use isolation forest model. But how to evaluate my model? What metrics should I follow?
If your 'features' are categorical, don't label encode then. Label encoding is meant for Target variables.
Evaluating models can be done as you would with any other predictive model
@@machinelearningplus but I don’t have a target variable and all the data is categorical how do you think I can proceed?? Btw thanks for your reply
@@ashrithadepu one hot encoding, that'all
🙂 Promo sm