ML Breath

[C3.ai] Model Building End-To-End

[Uber] L1 & L2 Regularization

[Google] Regression Model Metrics

[Amazon] Euclidean and Manhattan Distances

[Capital One] Feature Selection

[Amazon] Underfitting and Overfitting

[Google] Model Evaluation

[Uber] Explain AUC

[Amazon] The Curse of Dimensionality

[Capital One] Handling Missing Values

[Amazon] Hyperparameter Tuning

[Salesforce] Outliers in Modeling

[PayPal] Imbalanced Labels

[Google] Multicollinearity

[Amazon] Cross-Validation

[Amazon] Variance-Bias Trade-Off

[Amazon] Model Ensembling

[Amazon] Handling Categorical Variables

[Meta] Feature Engineering

[Microsoft] Text Features

ML Algorithm

[Microsoft] Finding Optimal K in K-Means

[Amazon] GBTs Hyperparameters

[Amazon] GBTs Pseudocode

[Apple] KNN Pseudocode

[Microsoft] K-Means

[Microsoft] Principal Component Analysis

[Salesforce] K-Means on Scaled and Unscaled Data

[Intuit] Variance & Bias in Trees

[Amazon] Bagging vs Boosting

[PayPal] Random Forest vs Logistic Regression

[Meta] Decision Tree Pseudocode

[Intuit] Logistic Regression Pseudocode

[Google] How do you evaluate regression modeling?

[Meta] How Is the random forest model trained?

Deep Learning

[PayPal] Activation Functions

[Microsoft] Gradient Descent

[Microsoft] Backpropagation

[Google] RNN vs CNN

[Amazon] Neural Network Overfitting

[Intuit] Encoder-Decoder

[Google] Tuning Dense Neural Networks

[Google] Normalization

[Google] Optimizers

ML Production

[Intuit] Kubernetes in Model Deployment

[Capital One] Model Failure in Production

[Intuit] Model Deployment on AWS

[Google] Online vs Offline Model Performance

[PayPal] Prediction Point

[Capital One] Model Deployment Challenges

[Intuit] Model Orchestration

Machine Learning

Last Updated:&nbsp;April 2024

<h2 class="wp-block-heading">Problem</h2>

How do L1 and L2 regularization terms work?

<hr>

<h2 class="wp-block-heading">Solution</h2>

L1 and L2 regularization are techniques used in machine learning to prevent overfitting. An overfit model learns the training data too well, adapting to noise and peculiarities, which leads to poor performance on new, unseen data.

Here’s how they work:

L1 Regularization (Lasso)

<ul class="wp-block-list">
<li>Concept:&nbsp;L1 regularization adds a penalty term to the model’s cost function that is proportional to the&nbsp;absolute value

Last Updated : &nbsp;April 2024 Problem How do L1 and L2 regularization terms work? Solution L1 and L2 regularization are techniques used in machine…

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[Uber] L1 & L2 Regularization

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