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Linear Graphs

Machine Learning often uses line graphs to show relationships.

A line graph displays the values of a linear function: y = ax + b

Important keywords

  • Linear (Straight)
  • Slope (Angle)
  • Intercept (Start value)

Linear

Linear means straight. A linear graph is a straight line.

The graph consists of two axes: x-axis (horizontal) and y-axis (vertical).

Example

const xValues = [];
const yValues = [];
// Generate values
for (let x = 0; x <= 10; x += 1) {
  xValues.push(x);
  yValues.push(x);
}
// Define Data
const data = [{
    x: xValues, y: yValues, mode: "lines"
  }];
// Define Layout
const layout = {title: "y = x"};
// Display using Plotly Plotly.newPlot("myPlot", data, layout);

Slope

The slope is the angle of the graph.

The slope is the a value in a linear graph:

y = a x

In this example, slope = 1.2 :

Example

let slope = 1.2;
const xValues = [];
const yValues = [];
// Generate values
for (let x = 0; x <= 10; x += 1) {
  xValues.push(x);
  yValues.push(x * slope);
}
// Define Data
const data = [{
    x: xValues, y: yValues, mode: "lines"
  }];
// Define Layout
const layout = {title: "Slope=" + slope};
// Display using Plotly Plotly.newPlot("myPlot", data, layout);

Intercept

The Intercept is the start value of the graph.

The intercept is the b value in a linear graph:

y = ax + b

In this example, slope = 1.2 and intercept = 7 :

Example

let slope = 1.2;
let intercept = 7;
const xValues = [];
const yValues = [];
// Generate values
for (let x = 0; x <= 10; x += 1) {
  xValues.push(x);
  yValues.push(x * slope + intercept);
}
// Define Data
const data = [{
    x: xValues, y: yValues, mode: "lines"
  }];
// Define Layout
const layout = {title: "Slope=" + slope + " Intercept=" + intercept};
// Display using Plotly Plotly.newPlot("myPlot", data, layout);

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Machine Learning in JavaScript

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Scatter Plots

This chapter

Overview
16

Lessons

69m

Read time

1. Machine Learning2. Artificial Intelligence3. Machine Learning Languages4. Machine Learning in JavaScript5. Linear Graphs6. Scatter Plots7. Perceptrons8. Pattern Recognition9. Testing a Perceptron10. Machine Learning11. ML Terminology12. Machine Learning Data13. Data Clusters14. Linear Regressions15. Deep Learning (DL)16. Brain.js

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