We use various techniques to achieve greater accuracy in machine learning models. One such method is ensemble learning. In this article, we will discuss the basics of ensemble learning. We will discuss the various ensembling techniques and the differences between them. What is Ensemble Learning? Ensemble learning is a technique to build machine learning applications…
We use different classification algorithms to build classifiers in machine learning. The naive Bayes classification algorithm is one of the easiest classification algorithms to understand and implement. In this article, we will discuss the Bayes algorithm and the intuition of Naive Bayes classification. We will also discuss a numerical example of Naive Bayes classification to…
Overfitting and Underfitting are two of the common issues that we face while training machine learning models. In this article, we will discuss overfitting and underfitting in machine learning. We will also discuss how to avoid overfitting and underfitting using various techniques. Before we start with a discussion on overfitting and underfitting, I suggest you…
When we train machine learning models, we might not always get very good results. We often run into situations where overfitting or underfitting occurs due to which the performance of the machine learning model deteriorates. This happens due to bias and variance errors. In this article, we will discuss these errors in machine learning and…
We often use categorical data encoding techniques such as label encoding and one hot encoding while data preprocessing. While these techniques offer an easy solution to convert categorical data to a numeric format, the representations are often inaccurate. In this article, we will discuss how to perform entity embedding to convert categorical data into a…
We use different categorical data encoding techniques while data analysis and machine learning tasks. In this article, we will discuss the basics of one hot encoding. We will also discuss implementing one hot encoding in Python. What is One Hot Encoding? One hot encoding is an encoding technique in which we represent categorical values with…
To analyze categorical data, we often need to convert them into numerical values. Label encoding is one of the most straightforward data preprocessing techniques for encoding categorical data into numeric values. This article will discuss different ways to perform label encoding in Python and pyspark. How to Perform Label Encoding? To perform label encoding, we…
Like the apriori algorithm, we also use the fp-growth algorithm to generate frequent itemsets from a transaction dataset in market basket analysis. This article will discuss how to implement the fp growth algorithm in Python. How to Implement The FP Growth Algorithm in Python? We will use the mlxtend module in Python to implement the…
To analyze categorical data, we need to convert them into numerical format. In this article, we will discuss different encoding techniques for converting categorical data into numeric format. How to Convert Categorical Data into Numerical Data? You can use the following encoding techniques to convert categorical into numeric data. Let us discuss all the categorical…
In data science, we work with data to produce insights that can help businesses solve problems. In real-world applications, most of the data is produced in a categorical format. Most of the attributes like gender, day of the week, names, etc can only be presented in textual or categorical format. On the contrary, most of…