ML-SPL Quick Reference Guide Machine Learning Toolkit http://tiny.cc/mlcheatsheet
Search Commands for Machine Learning
The Machine Learning Toolkit provides custom search commands for applying machine learning to your data.
Description
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Fit and apply a machine learning model to search results.
… | fit algorithm y from x params into model_name as output_field
Apply a machine learning model that was learned using the fit command.
… | apply model_name as output_field
Return a summary of a machine learning model that was learned using the fit command.
| summary model_name
listmodels
Return a list of machine learning models that were learned using the fit command.
| listmodels
deletemodel
Delete a machine learning model that was learned using the fit command.
| deletemodel model_name
Randomly sample or partition events.
… | sample options by split_by_field
Feature Extraction
Feature extraction algorithms transform fields for better prediction accuracy.
FieldSelector
…| fit FieldSelector type=categorical
SLA_violation from *
…| fit PCA * k=3
…| fit KernelPCA * k=3 gamma=0.001
…| fit TFIDF Reviews into
user_feedback_model max_def=0.6 min_def=0.2
algorithms are used for preparing data and help with prediction accuracy.
StandardScaler
.. | fit StandardScaler *
Cluster Numeric
Partition events with multiple numeric fields into clusters.
… | fit KMeans * k=3
… | fit DBSCAN *
… | fit Birch * k=3
SpectralClustering
… | fit SpectralClustering * k=3
Anomaly Detection
Find events that contain unusual combinations of values.
OneClassSVM
… | fit OneClassSVM * kernel=”poly”
nu=0.5coef0=0.5 gamma=0.5 tol=1 degree=3
shrinking=f into TESTMODEL_OneClassSVM
Forecasting
Forecast future values given past values of a metric (numeric time series).
… | fit ARIMA Voltage order=4-0-1
Predict Numeric
Predict the value of a numeric field using the values of other fields in that event.
LinearRegression
… | fit LinearRegression temperature from date_month date_hour into temperature_model
… | fit Lasso temperature from date_month date_hour
… | fit Ridge temperature from date_month date_hour normalize=true alpha=0.5
ElasticNet
… | fit ElasticNet temperature from date_month date_hour normalize=true alpha=0.5
KernelRidge
… | fit KernelRidge temperature from date_month date_hour into temperature_model
SGDRegressor
… | fit SGDRegressor temperature from date_month date_hour into temperature_model
DecisionTreeRegressor
… | fit DecisionTreeRegressor temperature from date_month date_hour into temperature_model
RandomForestRegressor
… | fit RandomForestRegressor temperature from date_month date_hour into temperature_model
Predict Categorical
Predict the value of a categorical field using the values of other fields in that event.
LogisticRegression
… | fit LogisticRegression SLA_violation from IO_wait_time into sla_model
… | fit SVM SLA_violation from * into sla_model
BernoulliNB
… | fit BernoulliNB type from * into TESTMODEL_BernoulliNB alpha=0.5 binarize=0 fit_prior=f
GaussianNB
… | fit GaussianNB species from * into TESTMODEL_GaussianNB
SGDClassifier
… | fit SGDClassifier SLA_violation from * into sla_model
DecisionTreeClassifier
… | fit DecisionTreeClassifier SLA_violation from * into sla_model
RandomForestClassifier
… | fit RandomForestClassifier SLA_violation from * into sla_model
ML-SPL Quick Reference Guide Machine Learning Toolkit
http://tiny.cc/mlcheatsheet
Predict Numeric Fields (Regression)
LinearRegression
ElasticNet
KernelRidge
SGDRegressor
DecisionTreeRegressor
RandomForestRegressor
Predict Categorical Fields (Classification)
LogisticRegression
BernoulliNB
GaussianNB
SGDClassifer
DecisionTreeClassifier
RandomForestClassifier
Categorical
Predict a Field Value
Detect Categorical Outliers
OneClassSVM
anomalydetection (command)
Forecast Future Values
Detect Outliers
Forecast Numeric Time Series
KalmanFilter (use predict command)
Detect Numeric Outliers
OneClassSVM
streamstats, median, mean, p25, p75
Group Events
Prepare Data
Cluster Numeric Events
SpectralClustering
Feature Extraction
FieldSelector
Preprocessing
StandardScaler
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