Find machine learning algorithms for your data.

Use Filters to describe your data or model requirements. Then look at the Applicable Models that match.

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Filters

Column types

What is the data type of your columns (a.k.a. “attributes” or “input / independent variables”)?

Columns can be:
numerical, e.g. "7.6"
binary, e.g. "true/false"
and categorical, e.g. "red/green/blue"

Target type

What is the data type of your target column (a.k.a. “label”, “class”, or “dependent variable”)? This is the column your model will predict.

Targets can be:
numerical, e.g. "7.6"
binary, e.g. "true/false"
or categorical, e.g. "red/green/blue"

It's fine to not have a target column!

Number of columns

Approximately how many columns does your data have (order of magnitude)?

Number of rows

Approximately how many rows does your data have (order of magnitude)?

Advanced options

Updatable:Select if your model should take new training data without the need to retrain on the complete data set.

Missings:Select if your model should handle missings values in the data.

Row weights:Select if your model should take the importance of rows into account to give those with a higher weight more emphasis during training.

Applicable Models