Interview Prep
Why does L1 regularisation lead to sparsity?
L1 regularization encourages sparsity by penalizing the absolute values of coefficients in a model. This penalty tends to shrink less important features towards zero, effectively removing them from the model and promoting a simpler, more interpretable solution.Can R square be negativeYes, R-squared can be negative. This occurs when the model performs worse than a model that simply predicts the mean of the target variable. It indicates that the chosen model is not a good fit for the data and performs worse than a naive baseline model. Negative R-squared values typically indicate severe overfitting or a poorly chosen model.What is curse of dimensionality?The curse of dimensionality refers to various problems that arise when dealing with high-dimensional data. As the number of features or dimensions increases, the volume of the space grows exponentially, leading to several challenges like sparse data, computation complexity, increased risk of overfitting and diminishing returns.Why does L1 regularisation lead to sparsity?L1 regularization encourages sparsity by penalizing the absolute values of coefficients in a model. This penalty tends to shrink less important features towards zero, effectively removing them from the model and promoting a simpler, more interpretable solution.Can R square be negativeYes, R-squared can be negative. This occurs when the model performs worse than a model that simply predicts the mean of the target variable. It indicates that the chosen model is not a good fit for the data and performs worse than a naive baseline model. Negative R-squared values typically indicate severe overfitting or a poorly chosen model.What is curse of dimensionality?The curse of dimensionality refers to various problems that arise when dealing with high-dimensional data. As the number of features or dimensions increases, the volume of the space grows exponentially, leading to several challenges like sparse data, computation complexity, increased risk of overfitting and diminishing returns.Why does L1 regularisation lead to sparsity?L1 regularization encourages sparsity by penalizing the absolute values of coefficients in a model. This penalty tends to shrink less important features towards zero, effectively removing them from the model and promoting a simpler, more interpretable solution.Can R square be negativeYes, R-squared can be negative. This occurs when the model performs worse than a model that simply predicts the mean of the target variable. It indicates that the chosen model is not a good fit for the data and performs worse than a naive baseline model. Negative R-squared values typically indicate severe overfitting or a poorly chosen model.What is curse of dimensionality?The curse of dimensionality refers to various problems that arise when dealing with high-dimensional data. As the number of features or dimensions increases, the volume of the space grows exponentially, leading to several challenges like sparse data, computation complexity, increased risk of overfitting and diminishing returns.Login to view more Questions
The author made these Questions available to DataPlay Members only.
© 2024 Dataplay.com. All rights reserved.
TESTIMONIALS
Our Students Speak
Transformative Testimonials from our Champions
Nishant was my mentor at data science training program. He is very knowledgeable data scientist. Nishant provided constructive and actionable mentorship during my training. He has in depth theoretical...Read More
Konstantin Lekomtsev
I enrolled in multiple courses earlier for Data science but I found Dataplay data science course to be the most comprehensive. Every topic is covered in depth with implementation on real life projects...Read More
Aditya Jain
I highly recommend DataPlay for anyone pursuing a career in data analysis and data science. The mentors are exceptionally dedicated, ensuring every student understands the concepts. The inclusive and ...Read More
Satyam Kumar