README.md
:- Fixed link to
black_friday
dataset
- Fixed link to
- Added code formatting checking step to CI pipline
- Corrected
README
examples - Added documentation to
Encoder
factory
ml_datframe
1.0.0 supportedfeatureNames
parameter renamed tocolumnNames
featureIds
parameter renamed tocolumnIndices
encodeAsIntegerLabels
renamed totoIntegerLabels
encodeAsOneHotLabels
renamed totoOneHotLabels
pubspec.yaml
:ml_dataframe
dependency updated
- Null-safety added (stable release)
- Null-safety added (beta release)
ml_dataframe
: version 0.4.0 supported
ml_dataframe
: version 0.3.0 supportedCI
: github actions set up
UnknownValueHandlingType
enum added to the lib's public API
ml_dataframe
0.2.0 supported
ml_dataframe
dependency updated
Standardizer
entity addeddtype
parameter added as an argument forPipeline.process
method
- Default values for parameters
headerPrefix
andheaderPostfix
added where it applicable
README
corrected (ml_dataframe version corrected)
xrange
dependency removedml_dataframe
0.0.11 supported
xrange
package version locked
Encoder
interface changed: there is no moreencode
method, useprocess
fromPipeable
insteadNormalizer
entity addednormalize
operator added
DataFrame
class split up into separate smaller entitiesDataFrame
class core moved to separate repositoryPipeline
entity created- Categorical data encoders implemented
Pipeable
interface
DataFrame
:encodedColumnRanges
added
ml_linalg
10.0.0 supported
ml_linalg
9.0.0 supported
Categorical data processing
:encoders
parameter added toDataFrame.fromCsv
constructor
xrange
library supported: it's possible to provideZRange
object now instead oftuple2
to specify a range of indices
DataFrame
introduced
Float32x4InterceptPreprocessor
addedreadme
updated
- Package published