Model Name

Final Model Name

Alphanumeric and underscores only. No spaces, special characters (, . / \ : ! @ # $ %, etc.)

Target

Target column

Prediction type

Note: you must convert your target column to numeric, e.g. (0,1) or (0.0,1.0) or (0,1,2,3,4) for Classification tasks

Disable Class Weights

Not Recommended. Class weights are row weights that are inversely proportional to the cardinality of its target class and help with class imbalance issues.

Train / Test Set

Train ratio

Proportion of the sample that goes to the train set. The rest goes to the test set

Random seed

Using a fixed random seed allows for reproducible result

Enable time ordering

Time ordering column

Metrics

Model Metric

Features selection

Feature Name	Type	Include?	Encoding / Rescaling	Impute Missing Values With	Constant Value (Impute)
{{ column.name }}	{{ column.type }}

Algorithms

Hover over algorithm or hyperparameter names for more info

Logistic Regression

C

(min)

(max)

Random Forest Classification

Number of trees

(min)

(max)

Max depth of tree

(min)

(max)

Min samples per leaf

(min)

(max)

XGBoost Classification

Number of trees

(min)

(max)

Max depth of tree

(min)

(max)

Min child weight

(min)

(max)

Learning rate

(min)

(max)

LightGBM Classification

Number of trees

(min)

(max)

Max depth of tree

(min)

(max)

Min child weight

(min)

(max)

Learning rate

(min)

(max)

Gradient Tree Boosting Classification

Number of trees

(min)

(max)

Max depth of tree

(min)

(max)

Min samples per leaf

(min)

(max)

Learning rate

(min)

(max)

Decision Tree Classification

Max depth of tree

(min)

(max)

Min samples per leaf

(min)

(max)

Lasso Regression

Alpha

(min)

(max)

Random Forest Regression

Number of trees

(min)

(max)

Max depth of tree

(min)

(max)

Min samples per leaf

(min)

(max)

XGBoost Regression

Number of trees

(min)

(max)

Max depth of tree

(min)

(max)

Min child weight

(min)

(max)

Learning rate

(min)

(max)

LightGBM Regression

Number of trees

(min)

(max)

Max depth of tree

(min)

(max)

Min child weight

(min)

(max)

Learning rate

(min)

(max)

Gradient Tree Boosting Regression

Number of trees

(min)

(max)

Max depth of tree

(min)

(max)

Min samples per leaf

(min)

(max)

Learning rate

(min)

(max)

Decision Tree Regression

Max depth of tree

(min)

(max)

Min samples per leaf

(min)

(max)

Search space limit

Maximum number of hyperparameter combinations to explore.

Snowflake Warehouse

If empty, use Snowflake connection default warehouse

Deploy to Snowflake ML Model Registry

Deploys model to the same database and schema as input train dataset. See Snowflake privilege requirements here: https://docs.snowflake.com/LIMITEDACCESS/snowflake-ml-model-registry