ML (machine learning) algorithms are often used to find feature relationships between features and labels. Features are the independent variables that we feed into the algorithm to train the ML model, and labels are the independent variables that we want to predict using the ML algorithm. In this short article, we will visualize ML algorithm with python:
Visualize Machine Leaning Algorithms in Python
Machine learning algorithm visualization means to visualize the trendline of the predicted values using the ML algorithm. What happens is when we train an ML model using the algorithm, we feed the data into the algorithm, the algorithm will find the relationship between the features and the labels.
So when we visualize an ML algorithm, it shows the trendline of the predicted label values according to the features. Now, we will train a simple machine learning algorithm and visualize it using Python. Here, we will first train a linear regression model to predict product sales based on that product’s TV ad spend, and then visualize the product’s predictions. linear regression algorithm using the diagram library in Python (you can download the data here: https://raw.githubusercontent.com/amankharwal/Website-data/master/Advertising.csv )
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression data = pd.read_csv("https://raw.githubusercontent.com/amankharwal/Website-data/master/Advertising.csv") x = data["TV"].values.reshape(-1,1) y = data["Sales"] model = LinearRegression().fit(x, y) x_range = np.linspace(x.min(), x.max(), 100) y_range = model.predict(x_range.reshape(-1,1)) import plotly.graph_objects as go import plotly.express as px figure = px.scatter(data, x = "TV", y = "Sales", opacity = 0.5) figure.add_trace(go.Scatter(x = x_range, y = y_range, name = "Linear Regression")) figure.show() |
In the image above, the red line is a trendline showing the predictions made by the linear regression algorithm, which is nothing more than the relationship between the amount spent on TV ads and product sales .
1 2 | print(y_range) |
The ML algorithm is used to find the relationship between features and labels. ML Algorithm Visualization means visualizing the trendline of the predicted values using a machine learning algorithm.
Source: https://thecleverprogrammer.com/