ml_preprocessing 7.0.1 ml_preprocessing: ^7.0.1 copied to clipboard
Popular data preprocessing algorithms for machine learning
Changelog #
7.0.1 #
- Added code formatting checking step to CI pipline
- Corrected
README
examples - Added documentation to
Encoder
factory
7.0.0 #
ml_datframe
1.0.0 supportedfeatureNames
parameter renamed tocolumnNames
featureIds
parameter renamed tocolumnIndices
encodeAsIntegerLabels
renamed totoIntegerLabels
encodeAsOneHotLabels
renamed totoOneHotLabels
6.0.1 #
pubspec.yaml
:ml_dataframe
dependency updated
6.0.0 #
- Null-safety added (stable release)
6.0.0-nullsafety.0 #
- Null-safety added (beta release)
5.2.2 #
ml_dataframe
: version 0.4.0 supported
5.2.1 #
ml_dataframe
: version 0.3.0 supportedCI
: github actions set up
5.2.0 #
UnknownValueHandlingType
enum added to the lib's public API
5.1.2 #
ml_dataframe
0.2.0 supported
5.1.1 #
ml_dataframe
dependency updated
5.1.0 #
Standardizer
entity addeddtype
parameter added as an argument forPipeline.process
method
5.0.4 #
- Default values for parameters
headerPrefix
andheaderPostfix
added where it applicable
5.0.3 #
README
corrected (ml_dataframe version corrected)
5.0.2 #
xrange
dependency removedml_dataframe
0.0.11 supported
5.0.1 #
xrange
package version locked
5.0.0 #
Encoder
interface changed: there is no moreencode
method, useprocess
fromPipeable
insteadNormalizer
entity addednormalize
operator added
4.0.0 #
DataFrame
class split up into separate smaller entitiesDataFrame
class core moved to separate repositoryPipeline
entity created- Categorical data encoders implemented
Pipeable
interface
3.4.0 #
DataFrame
:encodedColumnRanges
added
3.3.0 #
ml_linalg
10.0.0 supported
3.2.0 #
ml_linalg
9.0.0 supported
3.1.0 #
Categorical data processing
:encoders
parameter added toDataFrame.fromCsv
constructor
3.0.0 #
xrange
library supported: it's possible to provideZRange
object now instead oftuple2
to specify a range of indices
2.0.0 #
DataFrame
introduced
1.1.0 #
Float32x4InterceptPreprocessor
addedreadme
updated
1.0.0 #
- Package published