From c19ab67874e465467fda9a36d74be8835ad8e5c6 Mon Sep 17 00:00:00 2001 From: Arthur DANJOU Date: Tue, 26 Nov 2024 14:17:24 +0100 Subject: [PATCH] Fix article --- content/portfolio/what-is-machine-learning.md | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/content/portfolio/what-is-machine-learning.md b/content/portfolio/what-is-machine-learning.md index 4aae8f1..f7742e9 100644 --- a/content/portfolio/what-is-machine-learning.md +++ b/content/portfolio/what-is-machine-learning.md @@ -68,9 +68,15 @@ Once the model type is defined, the next step is to delve into the full workflow A machine learning project generally follows these steps: -1. **Data Preparation**\* Splitting data into training and testing sets.* Preprocessing: scaling, handling missing values, etc. -2. **Model Training**\* Fitting the model on training data: `model.fit(X, y)`.* Optimising parameters and hyperparameters. -3. **Prediction and Evaluation**\* Making predictions on unseen data: `model.predict(X)`.* Comparing predictions ($$\hat{y}$$) with actual values ($$y$$). +1. **Data Preparation** + - Splitting data into training and testing sets. + - Preprocessing: scaling, handling missing values, etc. +2. **Model Training** + - Fitting the model on training data: `model.fit(X, y)`. + - Optimising parameters and hyperparameters. +3. **Prediction and Evaluation** + - Making predictions on unseen data: `model.predict(X)`. + - Comparing predictions ($$\hat{y}$$) with actual values ($$y$$). ![Modelization in Prog.png](/portfolio/ML/model.png)