From 6586abf4077a4549db75d6d139b68a8b9c64b79b Mon Sep 17 00:00:00 2001
From: Arthur Danjou <arthurdanjou@outlook.fr>
Date: Tue, 26 Nov 2024 14:13:39 +0100
Subject: [PATCH] Change page title

---
 content/portfolio/what-is-machine-learning.md | 19 ++++++++-----------
 1 file changed, 8 insertions(+), 11 deletions(-)

diff --git a/content/portfolio/what-is-machine-learning.md b/content/portfolio/what-is-machine-learning.md
index c5afc43..18fd953 100644
--- a/content/portfolio/what-is-machine-learning.md
+++ b/content/portfolio/what-is-machine-learning.md
@@ -9,7 +9,7 @@ tags:
   - ML
 ---
 
-# What Is Machine Learning page
+# What Is Machine Learning ?
 
 ## Introduction
 
@@ -68,15 +68,12 @@ 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)
 
@@ -86,7 +83,7 @@ Evaluation is a crucial step to verify the performance of a model. For regressio
 
 For regression problems, the **R² score** measures the proportion of the target’s variance explained by the model:
 
-$$R2 = 1 - \frac{\text{SS}_{\text{residual}}}{\text{SS}_{\text{total}}}$$   where:
+$$R2 = 1 - \frac{\text{SS}_{\text{residual}}}{\text{SS}_{\text{total}}}$$ where:
 
 - $$\text{SS}\_{\text{residual}}$$ : Sum of squared residuals between actual and predicted values.
 - $$\text{SS}\_{\text{total}}$$ : Total sum of squares relative to the target’s mean.