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;August 2021</em></p>

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

<p>What is cross-validation? How does it work?</p>

<hr>

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

<p>Suppose that you randomly sample and allocate 70% of your data for training the model and the remaining 30% of the data for testing your model. Let’s also assume that the prediction problem is regression so mean squared error (MSE) is used to evaluate the model. You compute the MSE of the model prediction on testing data and conclude 678.34.&nbsp;</p>

<p>This approach seems to work, but there is a drawback.&nbsp;</p>

Last Updated : &nbsp;August 2021 Problem What is cross-validation? How does it work? Solution Suppose that you randomly sample and allocate 70% of your…

DataInterview

[Amazon] Cross-Validation

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