The L1 regularized logistic regression model has the property of shrinking the weights of irrelevant features to zero.
This penalty prevents overfitting by shrinking the weights towards zero, ensuring that, even in case of multicollinearity and P ≫ N, the estimator has a solution.
By then LendingTree's finances had shrunk under the weight of subprime and nonconforming loans.
The movie business, because it makes its living on big fat video files that are harder to share than audio files, was able to watch and learn as the music industry shrank under the weight of pirated downloads and then reluctantly embraced a 99-cent solution from Mr. Jobs.
He looks for decisive players who conform to the team ethos, and who grow rather than shrink with the weight of expectation that will come a year from now when almost 200 million Brazilians will be either on their side or, if it goes wrong, on their backs.
This perverse line of thought is largely responsible for a society where the income gap continues to widen, where the middle-class, a term that has been AWOL from the Republican discourse, and seemingly removed from the Republican dictionary altogether at this juncture, continues to shrink under the weight of wrong-headed and senseless economic and fiscal priorities.
He wanted to shrink the size of government, reduce the weight of taxes and disentangle America from unnecessary foreign involvement.
"Hey, boy!" he shouts, condescendingly, and Billy shrinks from the weight of his disapproval.
But with each year that shard shrinks, under the weight of gentrification, corporations and police.
Increasing λ shrinks the individual weights of the features towards zero, thereby decreasing the ratio | ω|1/max |ω|1.
