Hierarchical Clustering Algorithm
Hierarchical Clustering is a type of unsupervised machine learning algorithm used to group similar data points together. The goal of this algorithm is to create a hierarchy of clusters, where each cluster is a subset of the previous one. The algorithm starts by treating each data point as its own cluster. It then repeatedly merges…
How to handle categorical data in machine learning
Understanding Categorical Data and its Importance in Machine Learning Categorical data is a type of data that can be divided into distinct groups or categories. In machine learning, it is common to encounter categorical data in the form of labels, such as a classification problem where the output is a set of predefined categories. Handling…
K-Means Clustering Algorithm
K-Means is a widely used clustering algorithm that partitions a set of data points into K clusters, where each cluster is defined by its centroid. The goal of the algorithm is to minimize the sum of squared distances between each data point and its closest centroid. The algorithm starts by randomly selecting K initial centroids…
How to connect OpenAI api with python code
To connect OpenAI API with Python code, you will need to use the OpenAI Python library, which can be installed using pip: You will also need to have an API key for the OpenAI service you want to use. You can get an API key by creating an account on the OpenAI website. Once you…
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About Me
Hi, I’m Lillie. Previously a magazine editor, I became a full-time mother and freelance writer in 2017. I spend most of my time with my kids and husband over at The Brown Bear Family but this blog is for my love of food and sharing my favorites with you!
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Random Forest Algorithm
Random Forest is a robust machine-learning algorithm that is used for both classification and regression tasks. It is a type of ensemble learning method, which means that it combines multiple decision trees to create a more accurate and stable model. The mathematical intuition behind Random Forest is rooted in the concept of decision trees and…
Decision Tree
Decision tree algorithms are a type of supervised learning algorithm used to solve both regression and classification problems. The goal is to create a model that predicts the value of a target variable based on several input variables. Decision trees use a tree-like model of decisions and their possible consequences, including chance event outcomes, resource…
Support Vector Machine
Support Vector Machines (SVM) is a supervised machine learning algorithm that can be used for classification or regression tasks. The goal of the SVM algorithm is to find the hyperplane in an N-dimensional space that maximally separates the two classes. Mathematical Intuition Support Vector Machines (SVMs) are a type of supervised machine learning algorithm that…
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Steps to Create a Tensorflow Model
There are 3 fundamental steps to creating a model Create a Model -> Connect the layers of NN yourself by using Sequential or Functional API or import a previously built model(Transfer Learning)Compile a Model -> Define how a model’s performance should be measured(metrics) and how to improve it by using an optimizer(Adam, SGD, etc.)Fit a…
How to deal with outliers
In this Notebook, we will describe how to deal with outliers Trimming outliers from the dataset Performing winsorization Winsorizing is different from trimming because the extreme values are not removed, but are instead replaced byother values. Data greater than quantile 90 percent is replaced by value at 90 quantiles similarly less thenquantile 5 percent is…
What is data leakage in Machine Learning
When training a machine learning model, we normally prefer selecting a generalized model which is performing well both on training and validation/test data. However, there can be a situation where the model performs well during testing but fails to achieve the same level of performance with real-world (production data) usage. For example, your model is…