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| -rw-r--r-- | docs/mllib-linear-methods.md | 9 |
1 files changed, 5 insertions, 4 deletions
diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md index 6fcd3ae857..816bdf1317 100644 --- a/docs/mllib-linear-methods.md +++ b/docs/mllib-linear-methods.md @@ -78,6 +78,11 @@ methods `spark.mllib` supports: </tbody> </table> +Note that, in the mathematical formulation above, a binary label $y$ is denoted as either +$+1$ (positive) or $-1$ (negative), which is convenient for the formulation. +*However*, the negative label is represented by $0$ in `spark.mllib` instead of $-1$, to be consistent with +multiclass labeling. + ### Regularizers The purpose of the @@ -136,10 +141,6 @@ multiclass classification problems. For both methods, `spark.mllib` supports L1 and L2 regularized variants. The training data set is represented by an RDD of [LabeledPoint](mllib-data-types.html) in MLlib, where labels are class indices starting from zero: $0, 1, 2, \ldots$. -Note that, in the mathematical formulation in this guide, a binary label $y$ is denoted as either -$+1$ (positive) or $-1$ (negative), which is convenient for the formulation. -*However*, the negative label is represented by $0$ in `spark.mllib` instead of $-1$, to be consistent with -multiclass labeling. ### Linear Support Vector Machines (SVMs) |