Towards AI

APRIL 14, 2023

The prediction is then done using a k-nearest neighbor method within the embedding space. Distance preserving embeddings: The name of this method is straightforward. The embedding space is generated by preserving the distances between the labels.

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Pickl AI

JULY 31, 2023

In this article, we will delve into the differences and characteristics of these two methods, shedding light on their unique advantages and use cases. Random Forest : Random Forest is an ensemble learning method that combines multiple Decision Trees to improve prediction accuracy and reduce overfitting.

Machine Learning 52

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Mlearning.ai

APRIL 6, 2023

In this article, we will discuss some of the factors to consider while selecting a classification & Regression machine learning algorithm based on the characteristics of the data. For example, if you have binary or categorical data, you may want to consider using algorithms such as Logistic Regression, Decision Trees, or Random Forests.

Machine Learning 52

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Mlearning.ai

FEBRUARY 17, 2023

In this article, I will cover all of them. Simple linear regression Multiple linear regression Polynomial regression Decision Tree regression Support Vector regression Random Forest regression Classification is a technique to predict a category. It’s a fantastic world, trust me!

Machine Learning 52

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ODSC - Open Data Science

JANUARY 5, 2024

In this article, I will provide my top five reasons for using the Seaborn library to create data visualizations with Python. Originally posted on OpenDataScience.com Read more data science articles on OpenDataScience.com , including tutorials and guides from beginner to advanced levels! Not a bad list right?

Python 52

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Pickl AI

NOVEMBER 18, 2024

The article also addresses challenges like data quality and model complexity, highlighting the importance of ethical considerations in Machine Learning applications. For example, linear regression is typically used to predict continuous variables, while decision trees are great for classification and regression tasks.

Machine Learning 52

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Towards AI

JULY 19, 2023

Dealing with imbalanced data is pretty common in the real-world and these articles by German Lahera and on DataCamp are good places to learn about them. Feel free to try other algorithms such as Random Forests, Decision Trees, Neural Networks, etc., among supervised models and k-nearest neighbors, DBSCAN, etc.,

Machine Learning 40

Machine Learning Machine Learning K-nearest Neighbors Support Vector Machines 40

Pickl AI

SEPTEMBER 12, 2024

Summary : This article equips Data Analysts with a solid foundation of key Data Science terms, from A to Z. By understanding crucial concepts like Machine Learning, Data Mining, and Predictive Modelling, analysts can communicate effectively, collaborate with cross-functional teams, and make informed decisions that drive business success.

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Pickl AI

JULY 26, 2023

Here are some examples of variance in machine learning: Overfitting in Decision Trees Decision trees can exhibit high variance if they are allowed to grow too deep, capturing noise and outliers in the training data.

Machine Learning 52

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Mlearning.ai

FEBRUARY 15, 2023

Some important things that were considered during these selections were: Random Forest : The ultimate feature importance in a Random forest is the average of all decision tree feature importance. A random forest is an ensemble classifier that makes predictions using a variety of decision trees.

Cross Validation 52

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Mlearning.ai

MAY 23, 2023

In this article, we will explore some common data science interview questions that will help you prepare and increase your chances of success. Decision trees are more prone to overfitting. Some algorithms that have low bias are Decision Trees, SVM, etc. Variance: Variance is also a kind of error.

Data Science 52

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The MLOps Blog

DECEMBER 19, 2022

In this article, we will talk about feasible techniques to deal with such a large-scale ML Classification model. In this article, you will learn: 1 What are some examples of large-scale ML classification models? index.add(xb) # xq are query vectors, for which we need to search in xb to find the k nearest neighbors. #

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DagsHub

DECEMBER 23, 2024

They are: Based on shallow, simple, and interpretable machine learning models like support vector machines (SVMs), decision trees, or k-nearest neighbors (kNN). Relies on explicit decision boundaries or feature representations for sample selection. different architectures or initializations).

Deep Learning 52

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Dataconomy

DECEMBER 12, 2024

The figure below shows the byte distribution of some file types: Please note that all codes and input/output data examples in this article are derived from Clarity Consultings MigNon tool. Overfitting can occur when the model uses too many features, causing it to make decisions faster, for example, at the endpoints of decision trees.

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