Machine Learning (Page 2)

What Are Hyperparameters? When a model is being trained on a data set, it determines the best parameters for each predictor variable. This can be seen in a linear regression, where the coefficients are determined for each variable used in the model. The coefficients in this case are the parameters:Continue Reading

Cross-validation is a technique for validating the model efficiency by training it on the subset of input data and testing on previously unseen subset of the input data. We can also say that it is a technique to check how a statistical model generalizes to an independent dataset. In machine learning, thereContinue Reading

Now we will implement the Random Forest Algorithm tree using Python. For this, we will use the same dataset “user_data.csv”, which we have used in previous classification models. By using the same dataset, we can compare the Random Forest classifier with other classification models such as Decision tree Classifier, KNN, SVM, Logistic Regression, etc. ImplementationContinue Reading

Random Forest is a popular machine learning algorithm that belongs to the supervised learning technique. It can be used for both Classification and Regression problems in ML. It is based on the concept of ensemble learning, which is a process of combining multiple classifiers to solve a complex problem and to improve theContinue Reading

Now we will implement the Decision tree using Python. For this, we will use the dataset “user_data.csv,” which we have used in previous classification models. By using the same dataset, we can compare the Decision tree classifier with other classification models such as KNN SVM, Logistic Regression, etc. Steps will also remain the same,Continue Reading

Decision Tree is a Supervised learning technique that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems. It is a tree-structured classifier, where internal nodes represent the features of a dataset, branches represent the decision rules and each leaf node represents the outcome. In a DecisionContinue Reading

Now we will implement a Naive Bayes Algorithm using Python. So for this, we will use the “user_data” dataset, which we have used in our other classification model. Therefore we can easily compare the Naive Bayes model with the other models. Steps to implement: Data Pre-processing step Fitting Naive Bayes toContinue Reading

Naïve Bayes Classifier Algorithm Naïve Bayes algorithm is a supervised learning algorithm, which is based on Bayes theorem and used for solving classification problems. It is mainly used in text classification that includes a high-dimensional training dataset. Naïve Bayes Classifier is one of the simple and most effective Classification algorithms which helps in buildingContinue Reading

Python implementation of the KNN algorithm To do the Python implementation of the K-NN algorithm, we will use the same problem and dataset which we have used in Logistic Regression. But here we will improve the performance of the model. Below is the problem description: Problem for K-NN Algorithm: There isContinue Reading