Model Validation for Supervised Models

  1. Choose a class of model
  2. Choose model hyperparameters
  3. Fit the model to the training data
  4. Use the model to predict labels for new data

Exploring Model Validation

Holdout Sets

Cross-Validation

Selecting the Best Model

  • Use a more complicated/more flexible model
  • Use a less complicated/less flexible model
  • Gather more training samples
  • Gather more data to add features to each sample

--

--

--

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

3D object classification in 6 steps using kaolin and Colab

Which Mask Are You Wearing? Face Mask Type Detection with TensorFlow and Raspberry Pi

DESCRIPTIVE STATISTICS

Movie Ratings Analysis

Medical Image Analysis with Deep Learning — I

Modeling of an Autonomous AI HVAC Control System Commissioning

How to Think Like a Data Scientist: A Blog about what a Data Scientist does and How a Data…

DronaFarms: Second Wave of Green Revolution powered by grassroots data

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Poshan Pandey

Poshan Pandey

More from Medium

Time Series Steady-State Detection for Industrial Applications

A “Practical Data Science” Approach to Detecting Meteors with CAMS

React, Prevent, Predict: Field Service Maintenance Strategies | Gruntify

Analyzing Airport Delays using Tableau