Ridge regression is similar to linear regression but it uses L2 regularization to reduce overfitting and increase model generalizability. We can quickly implement ridge regression in Python using the Sklearn library. In this tutorial, I will use the diabetes dataset for ridge regression because it is already available in Sklearn. I highly recommend this book for machine …
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Linear Regression is a supervised machine-learning algorithm. Linear regression is used for regression problems. We can quickly implement multiple linear regression in Python using the Sklearn library. You have to follow the given steps to implement the multiple linear regression. In this tutorial, I will use this dataset for multiple linear regression. It is available …
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A stochastic gradient descent (SGD) classifier is an optimization algorithm. It is used to minimize the cost by finding the optimal values of parameters. We can use it for classification problems. We can quickly implement the SGD classifier in Python using the Sklearn library. You have to follow the given steps to implement the stochastic …
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Logistic Regression is a supervised machine-learning algorithm. Logistic regression is used for classification problems. It predicts the probability of the target variable. We can quickly implement logistic regression in Python using the Sklearn library. You have to follow the given steps to implement the logistic regression. Step 1: Import the libraries Step 2: Import the …
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Random Forest is a supervised machine-learning algorithm. A random forest can be used for classification and regression problems. Random Forest is mostly used for classification tasks. It has multiple decision trees for various subsets of the given dataset. We can quickly implement Random Forest in Python using the Sklearn library. You have to follow the …
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A decision tree is a supervised machine-learning algorithm. A decision tree can be used for classification and regression. It is mostly used for classification tasks. It looks like a tree structure. We can quickly implement a Decision Tree in Python using the Sklearn library. You have to follow the given steps to implement the decision …
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Naive Bayes classifiers are supervised machine learning algorithms. The Naive Bayes algorithms are based on Bayes’ theorem. We can quickly implement the Naive Bayes classifier in Python using the Sklearn library. You have to follow the given steps. Step 1: Import the libraries Step 2: Import the iris dataset sepal_length sepal_width petal_length petal_width species 0 …
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SVM (Support Vector Machine) is the most popular supervised machine learning algorithm. The SVM algorithm can be used for classification, regression, and outliers detection. We can quickly implement SVM in Python using the Sklearn library. You have to follow the given steps. Step 1: Import the libraries Step 2: Import the iris dataset sepal_length sepal_width …
KNN (k-nearest neighbor) is a supervised machine learning algorithm. The KNN algorithm can be used for classification and regression. We can quickly implement KNN in Python using the Sklearn library. You have to follow the given steps. Step 1: Import the libraries Step 2: Import the iris dataset sepal_length sepal_width petal_length petal_width species 0 5.1 …
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In this article, you’ll see the implementation of a train-test split in Python using Sklearn. If you want to learn machine learning, I recommend you get the “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” book. In this example, we will use the iris dataset. There are 150 rows and 5 columns in this dataset. …
