Anomaly_Detection
When you set all parameters needded for clustering, including data processing, now you are ready to start training your model.
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When you set all parameters needded for clustering, including data processing, now you are ready to start training your model.
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Was this helpful?
This section is for training models where the number of clusters is already defined.
Click Step 1 : Train , and the training process will start, you will get this type of output
After your model is trained, click Step 2 : Assign anomalies , and anomalies will be assigned to data, you get the following output.
The Model will be saved as Anomaly_Detection_Model_15.pkl
The assigned data will be saved under the file name predicted_data_Assigned_15.csv
15 is the session ID number.
This section is for is for training model when data is already labeled. and tune fraction fraction parameter of the model.
Fill the following multiselect boxes.
Select the target column containing labels : Name of the target column containing labels.
Select type of task (Automatically inferred when None): Choose from the list
if Classification:
‘ Logistic Regression (Default)
K Nearest Neighbour
Naive Bayes
Decision Tree Classifier
SVM - Linear Kernel
SVM - Radial Kernel
Gaussian Process Classifier
Multi Level Perceptron
Ridge Classifier
Random Forest Classifier
Quadratic Discriminant Analysis
Ada Boost Classifier
Gradient Boosting Classifier
Linear Discriminant Analysis
Extra Trees Classifier
Extreme Gradient Boosting
Light Gradient Boosting
CatBoost Classifier
if Regression:
Linear Regression (Default)
Lasso Regression
Ridge Regression
Elastic Net
Least Angle Regression
Lasso Least Angle Regression
Orthogonal Matching Pursuit
Bayesian Ridge
Automatic Relevance Determ.
Passive Aggressive Regressor
Random Sample Consensus
TheilSen Regressor
Huber Regressor
Kernel Ridge
Support Vector Machine
K Neighbors Regressor
Decision Tree
Random Forest
Extra Trees Regressor
AdaBoost Regressor
Gradient Boosting
Multi Level Perceptron
Extreme Gradient Boosting
Light Gradient Boosting
CatBoost Regressor
Select the evaluation metric: For Classification tasks: Accuracy, AUC, Recall, Precision, F1, Kappa (default = ‘Accuracy’), For Regression tasks: MAE, MSE, RMSE, R2, RMSLE, MAPE (default = ‘R2’).
Select the method of labeling outliers (default = drop) : When method set to drop, it will drop the outliers from training dataset. When surrogate, it uses decision function and label as a feature during training.
Select the number of folds to be used in cross validation Number of folds to be used in Kfold CV. Must be at least 2.
Click Step 1 : Tune the fraction parameter and Evaluate button, to get the following output:
Click Step 2 : Assign anomalies to assign anomalies to assign data with new tuned model.the output will look like this:
The model will be saved as Anomaly_Detection_Model_16_tuned.pkl
Assigned data will be saved under the file name predicted_data_16_tuned.csv
with session ID equal 16 in this case.
