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

<p><strong>Last Updated</strong><em><strong>:</strong>&nbsp;April 2024</em></p>

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

<p>How do you evaluate whether a model is underfitting or overfitting?</p>

<hr>

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

<p>A way to evaluate if a model has underfitted or overfitted is to look at the model’s error curves on train and validation datasets as seen below. Consider the number of trees (iterations) sequentially constructed in the XGBoost model. As iterations increase, the errors of both train and validation are in a downward trajectory. This is an indication that the model has underfitted and there’s still &nbsp;room to identify the optimal fit.</p>

Last Updated : &nbsp;April 2024 Problem How do you evaluate whether a model is underfitting or overfitting? Solution A way to evaluate if a model has…

DataInterview

[Amazon] Underfitting and Overfitting

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