There are many concepts in machine learning in which a beginner should be perfect. One of these concepts is the training of a machine learning model. So if you’ve never trained a machine learning model before, this article is for you. In this article, I’ll walk you through how to train a machine learning model using Python.
Why Do We Need to Train a Machine Learning Model?
Most concepts in machine learning revolve around training a machine learning model, but why does a model need to be trained? The answer is that we train a model to find relationships between the independent variables and the dependent variable so that we can predict future values of the dependent variable.
So, the only idea behind the training of machine learning models is to find the relationships between the independent variables (x) and the dependent variable (y). So if you are new to machine learning, you must have heard of model training. In the section below, I’ll walk you through how to train your first machine learning model using Python.
Train a Machine Learning Model using Python
To train your very first machine learning model, you need to have a dataset. Assuming you’re a beginner, I’m not going to walk you through a very complex dataset right now. So here I will be using the classic Iris data which is a very famous dataset among data science newbies. So let’s import the necessary Python libraries and dataset that we need for this task:
import numpy as np from sklearn.datasets import load_iris from sklearn.linear_model import LogisticRegression iris = load_iris()
Now, I’m going to split this data into two parts (x and y), in x I’m going to store the independent variables we need to predict y, and in y, I’m going to store the target variable (also known as a label):
x = iris.data[:,(2,3)] #petal length, petal width y = (iris.target).astype(np.int)
Now the next step is to choose a machine learning algorithm. As this problem is classification based, I will simply use the logistic regression algorithm here. So here’s how we train a machine learning model:
model = LogisticRegression() model.fit(x, y)
We just fit the features x and the target label y to the model by using the model.fit() method provided by the scikit-learn library in Python.
So this is how you can easily train machine learning models. The next steps you can take are to start training models on a more complex dataset. Of course, complex data has to be prepared a lot, but once prepared, go through the same process again. You can find over 200 machine learning projects solved and explained from here. Now you can learn to train many machine learning models with different types of problems and datasets. Hope you liked this article on how to train machine learning models. Please feel free to ask your valuable questions in the comments section below.