Questions tagged [lasso]

Least Absolute Shrinkage and Selection Operator (LASSO) regression, is a regularization technique used in regression cases where the model overfits or there is high multi-collinearity.

Filter by
Sorted by
Tagged with
0 votes
0 answers
17 views

Lasso with cross validation - is training/test set split nedeed?

I am using LASSO regression to identify the most important variables for predicting an outcome. I have a relatively small sample (~1000 observations), and I was planning to use cross-validation to ...
user avatar
  • 1
0 votes
0 answers
8 views

Regularizing the intercept - particular case

Yesterday I posted this thread Regularizing the intercept where I had a question about penalizing the intercept. In short, I asked wether there exist cases where penalizing the intercept leads to a ...
user avatar
0 votes
1 answer
35 views

How to compare between two methods of multivariate to filling NA

In the Titanic dataset, I performed two methods to fill Age NA. The first one is regression using Lasso: ...
user avatar
0 votes
1 answer
9 views

How to extract MSEP or RMSEP from lassoCV?

I'm doing lasso and ridge regression in R with the package chemometrics. With ridgeCV it is easy to extract the SEP and MSEP values by ...
user avatar
  • 5
1 vote
0 answers
40 views

Why is the L2 penalty squared but the L1 penalty isn't in elastic-net regression?

There was some data set I worked with which I wanted to solve non negative least squares (NNLS) on and I wanted a sparse model. After a bit of experiementing I found that what worked the best for me ...
user avatar
0 votes
1 answer
149 views

Why is gridsearchCV.best_estimator_.score giving me r2_score even if I mentioned MAE as my main scoring metric?

I have a lasso regression model with the following definition : ...
user avatar
  • 103
1 vote
1 answer
38 views

Is it possible to explain why Lasso models eliminated certain coefficient?

Is it possible to understand why Lasso models eliminated specific coefficients?. During the modelling, many of the highly correlated features in data is being eliminated by Lasso regression. Is it ...
user avatar
  • 209
0 votes
0 answers
27 views

Lasso (or Ridge) vs Bayesian MAP

This is the first time I have posted here. I am looking for some feedback or perspective on this question. To make it simple, let's just talk about linear models. We know the MLE solution for the $l_1$...
user avatar
0 votes
1 answer
58 views

Accessing regression coefficients when using MultiOutputRegressor

I am working on a multioutput (nr. targets: 2) regression task. The original data has a huge dimensionality (p>>n, i.e. there are far more predictors than ...
user avatar
  • 259
1 vote
0 answers
49 views

Predicting single floats based on set of 2 feature arrays each of 100 values

I am trying to predict audio to video desynchronization based on set of two arrays of lenght 100 which consist of coresponding audio and video samples. The problem is that my labels are single floats (...
user avatar
1 vote
1 answer
23 views

Why are we not checking the significance of the coefficients in Lasso and elastic net models

As far as I know, we don't check the coefficient significance in Lasso and elasticnet models. Is it because insignificant feature coefficients will be driven to zero in these models?. Does that mean ...
user avatar
  • 209
1 vote
1 answer
136 views

Elegant way to plot the L2 regularization path of logistic regression in python?

Trying to plot the L2 regularization path of logistic regression with the following code (an example of regularization path can be found in page 65 of the ML textbook Elements of Statistical Learning ...
user avatar
0 votes
1 answer
23 views

Do I have to remove features with pairwise correlation even if I am doing a regularized logistic regression?

Normally we would remove features that have high pairwise correlation with another feature before performing regression. But is this step necessary if I am applying L2 regularized logistic regression (...
user avatar
2 votes
0 answers
74 views

Can I rescale TF matrix or TF-IDF matrix using StandardScaler prior to Logisitc Lasso regression?

I am trying to use Logistic Lasso to classify documents as 1 or 0. I've tried using both the TF matrix and TF-IDF matrix representations of the documents as my predictors. I've found that if I use the ...
user avatar
1 vote
1 answer
70 views

What's the correct cost function for Linear Regression

As we all know the cost function for linear regression is: Where as when we use Ridge Regression we simply add lambda*slope**2 but there I always seee the below as cost function of linear Regression ...
user avatar
  • 173
0 votes
0 answers
105 views

Can Adagrad or Adam be used in loss function with l1-norm regularization?

there is one question for me. I want to know that how Adam or Adagrad treat l1-norm regularization in loss-function? (e.g. Lasso) I know that l1-norm is not differentiable function at zero but we can ...
user avatar
  • 21
0 votes
0 answers
24 views

How to handle both the categorical and ordinal features in a single data sets?

I was practicing Lasso regression with the SPARCS hospital dataset. There are two kinds of features in the dataset: Categorical features like location of the hospital, demographics of patients, etc. ...
user avatar
0 votes
1 answer
48 views

How to set coefficient limit in lasso regression in Python?

I'm working on a regression problem where I want to use Lasso model. With the help of Lasso and LassoCV, we can provide different alpha values and get the best parameter and coefficients however I ...
user avatar
0 votes
0 answers
123 views

Should I use or tune `reg_lambda` or `reg_alpha` hyperparameters when using a tree booster in XGBoost

XGBoost has 3 types of boosters: tree boosters (gbtree, dart) linear booster (gbliner) ...
user avatar
  • 101
3 votes
1 answer
204 views

Difference between PCA and regularisation

Currently, I am confusing about PCA and regularisation. I wonder what is the difference between PCA and regularisation: particularly lasso (L1) regression? Seems both of them can do the feature ...
user avatar
  • 33
-1 votes
1 answer
100 views

Lack of standardization in Kaggle jupyter notebooks when using lasso/ridge?

I've recently started using Kaggle, and I've noticed that for a lot of these jupyter notebooks written by others, when they use Ridge/Lasso, they don't standardize the non-categorical numerical ...
user avatar
0 votes
0 answers
408 views

sklearn gridsearch lasso regression: find specific number of coefficients

I am using GridSearchCV and Lasso regression in order to fit a dataset composed out of Gaussians. I keep this example similar to this tutorial. My goal is to find the best solution with a restricted ...
user avatar
  • 1
1 vote
1 answer
71 views

Lasso regression not getting better without random features

First of all, I'm new to lasso regression, so sorry if this feels stupid. I'm trying to build a regression model and wanted to use lasso regression for feature selection as I have quite a few features ...
user avatar
0 votes
1 answer
681 views

How to remove features from a sklearn pipeline after it has already been fitted?

Background: I have created a basic modeling workflow in sklearn that utilizes sklearn's pipeline object. There are some preprocessing steps within the pipeline, and the last step of the pipeline is to ...
user avatar
1 vote
1 answer
98 views

Lasso Regression for Feature Importance saying almost every feature is unimportant?

I have a metric (RevenueSoFar) that is a great predictor of my target FinalRevenue as you'd expect - it is a metric where we tend to get 90-95% of revenue so far on day 1 and then it can increase over ...
user avatar
  • 113
2 votes
1 answer
31 views

Interpreting machine learning coefficients

My dog show predictive tool is having some trouble with its neural net. Broadly, I start with a couple of factors--age, weight, height, breed (which is a set of dummy variables), a subjective cuteness ...
user avatar
1 vote
2 answers
386 views

What is the meaning of the sparsity parameter

Sparse methods such as LASSO contain a parameter $\lambda$ which is associated with the minimization of the $l_1$ norm. Higher the value of $\lambda$ ($>0$) means that more coefficients will be ...
user avatar
  • 521
0 votes
2 answers
161 views

how Lasso regression helps to shrinks the coefficient to zero and why ridge regression dose not shrink the coefficient to zero?

How Lasso regression helps feature selection of model by making the coefficient to zero? , I could see few below with below diagram ,can any please explain in simple terms how to corelate below ...
user avatar
  • 1,181
0 votes
1 answer
657 views

regarding lasso.score in lasso modeling using scikit-learn

I once saw the following code segment of using lasso model based on scikit-learn ...
user avatar
1 vote
1 answer
37 views

Generating artificial data to extend learning set

I have dataset containing 42 instances(X) and one final Y on which i want to perform LASSO regression.All are continuous and numerical. As the sample size small, I wish to extend it. I am kind of ...
user avatar
  • 11
2 votes
2 answers
1k views

How do standardization and normalization impact the coefficients of linear models?

One benefit of creating a linear model is that you can look at the coefficients the model learns and interpret them. For example, you can see which features have the most predictive power and which do ...
user avatar
1 vote
1 answer
466 views

How is learning rate calculated in sklearn Lasso regression?

I was applying different regression models to Kaggle Housing dataset for advanced regression. I am planning to test out lasso, ridge and elastic net. However, none of these models have learning rate ...
user avatar
1 vote
0 answers
20 views

Lasso stricter with more data

I am currently analyzing investment strategies, and have implemented a backtest accordingly. This essentially means that I predict returns each month by using all prior historical data. Consequently, ...
user avatar
  • 111
4 votes
1 answer
106 views

For a square matrix of data, I achieve $R^2=1$ for Linear Regression and $R^2=0$ for Lasso. What's the intuition behind?

For a square matrix of random data, N columns and N rows. I am fitting two models, linear regression and Lasso. For the linear regression, I achieve a perfect score in train set, while in the Lasso I ...
user avatar
2 votes
1 answer
23 views

When should we start using stacking of models?

I am solving a Kaggle contest and my single model has reached score of 0.121, I'd like to know when to start using ensembling/stacking to improve the score. I used lasso and xgboost and there ...
user avatar
1 vote
1 answer
389 views

Normalisation results in R^2 score of 0 - Lasso regression

I am running a regression analysis on a 7000 row dataset with a train/test split of 70%/30%. I am using one variable X to predict a variable ...
user avatar
  • 11
1 vote
0 answers
41 views

Scikit-compatible network lasso implementation

Is anyone aware of a scikit-compatible network Lasso (nLasso) implementation? These papers have source code as well: D. Hallac, J. Leskovec, and S. Boyd, “Network lasso: Clustering and ...
user avatar
4 votes
2 answers
71 views

LASSO remaining features for different penalisation

I am using the sklearn LASSOCV function and I am changing the penalisation parameter in order to adjust the number of features killed off. For example for $\alpha = 0.01$ I have 55 features remaining ...
user avatar
4 votes
2 answers
345 views

Why does Lasso behave "erratically" when the number of features is greater than the number of training instances?

From the book "Hands-on Machine Learning with Scikit-Learn and TensorFlow 2nd edition" chapter 4: In general, Elastic Net is preferred over Lasso since Lasso may behave erratically when the ...
user avatar
2 votes
0 answers
92 views

Why do you need to use group lasso with categorical variables?

From what I've read you should you use group lasso to either discard the dummy encoded variables (of the category) or use all of them. If you use normal lasso then some of the variables in the group ...
user avatar
  • 121
0 votes
2 answers
217 views

What happens when scikit-learn does a Lasso Model?

I have started an MLS course. As a beginner and non-mathematician it has been hard. I am trying to understand the exercise about Lasso Models. I have done Lasso models on R-cran, but this is my first ...
user avatar
1 vote
0 answers
50 views

Lasso implementation Drawback

Recently I've been trying to implement Lasso by myself in R, not using the "glmnet" package, and based on an article by Tibshirani I wrote a raw code to implement coordinate descent method, and it ...
user avatar