diff options
| -rw-r--r-- | mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala | 8 |
1 files changed, 4 insertions, 4 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala index 401f2c673f..0a155e1844 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala @@ -794,16 +794,16 @@ class LinearRegressionSummary private[regression] ( * * Now, the first derivative of the objective function in scaled space is * {{{ - * \frac{\partial L}{\partial\w_i} = diff/N (x_i - \bar{x_i}) / \hat{x_i} + * \frac{\partial L}{\partial w_i} = diff/N (x_i - \bar{x_i}) / \hat{x_i} * }}} * However, ($x_i - \bar{x_i}$) will densify the computation, so it's not * an ideal formula when the training dataset is sparse format. * - * This can be addressed by adding the dense \bar{x_i} / \har{x_i} terms + * This can be addressed by adding the dense \bar{x_i} / \hat{x_i} terms * in the end by keeping the sum of diff. The first derivative of total * objective function from all the samples is * {{{ - * \frac{\partial L}{\partial\w_i} = + * \frac{\partial L}{\partial w_i} = * 1/N \sum_j diff_j (x_{ij} - \bar{x_i}) / \hat{x_i} * = 1/N ((\sum_j diff_j x_{ij} / \hat{x_i}) - diffSum \bar{x_i}) / \hat{x_i}) * = 1/N ((\sum_j diff_j x_{ij} / \hat{x_i}) + correction_i) @@ -822,7 +822,7 @@ class LinearRegressionSummary private[regression] ( * the training dataset, which can be easily computed in distributed fashion, and is * sparse format friendly. * {{{ - * \frac{\partial L}{\partial\w_i} = 1/N ((\sum_j diff_j x_{ij} / \hat{x_i}) + * \frac{\partial L}{\partial w_i} = 1/N ((\sum_j diff_j x_{ij} / \hat{x_i}) * }}}, * * @param coefficients The coefficients corresponding to the features. |