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Isomap Embedding — An Awesome Approach to Non-linear Dimensionality Reduction
Next in the series on Machine Learning algorithms a look at another dimensionality reduction technique known as Isometric Mapping or Isomap for short.
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What is Isomap?
We cannot visualize high-dimensional data above 3 dimensions. So what do we do when we are faced with this situation that is commonplace in nearly every Data Science application? Dimension reduction…
Read more at Towards Data SciencePreserving Geodesic Distance for Non-Linear Datasets: ISOMAP
This article includes an interpretation of ISOMAP results, python implementation of ISOMAP, differences between geodesic distance and Euclidean distance, usage areas of ISOMAP. The role of the ISOMAP ...
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Decomposing Non-linearity with ISOMAP
Many applications of data science involves dealing with High-Dimensional data like images. With such amount of multivariate data rises an underlying problem of visualizing them. To do so, we usually…
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Manifold Learning [t-SNE, LLE, Isomap, +] Made Easy
Principal Component Analysis is a powerful method, but it often fails in that it assumes that the data can be modelled linearly. PCA expressed new features as linear combinations of existing ones by…
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Dimensionality Reduction with Scikit-Learn: PCA Theory and Implementation
In the novel Flatland , characters living in a two-dimensional world find themselves perplexed and unable to comprehend when they encounter a three-dimensional being. I use this analogy to illustrate ...
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Advanced Dimensionality Reduction Models Made Simple
When approaching a Machine Learning task, have you ever felt stunned by the massive number of features ? Most Data Scientists experience this overwhelming challenge on a daily basis. While adding feat...
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Understanding Classification Thresholds Using Isocurves
Your job as a data scientist isn’t done until you explain how to interpret the model and apply it. That means threshold selection for the business decision that motivated the model.
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A Guide to Dimensionality Reduction in Python
Dimensionality reduction is the process of transforming high-dimensional data into a lower dimensional format while preserving the most important properties. This technique has applications in many…
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6.5. Unsupervised dimensionality reduction
If your number of features is high, it may be useful to reduce it with an unsupervised step prior to supervised steps. Many of the Unsupervised learning methods implement a transform method that ca......
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Unsupervised Regression for Dimensionality Reduction
Using regression to compute low-dimensional embeddings Image by Annie Spratt on Unsplash Every first-year student in data science and A.I. learns that regression is a supervised learning method. Orig...
Read more at Towards Data ScienceBuilding a k-Nearest Neighbors Classifier with Scikit-learn: A Step-by-Step Tutorial
Scikit-learn is a popular Python library for Machine Learning that provides tools for data analysis, data pre-processing, model selection… Continue reading on Level Up Coding
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Building a k-Nearest-Neighbors (k-NN) Model with Scikit-learn
k-Nearest-Neighbors (k-NN) is a supervised machine learning model. Supervised learning is when a model learns from data that is already labeled. A supervised learning model takes in a set of input…
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Dimensionality Reduction with Python
When building a machine learning model, most likely you will not use all the variables available in your training dataset. In fact, training datasets with hundreds or thousands of features are not…
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The Art of Dimensionality Reduction
Suppose you want to solve a predictive modeling problem, and for the same, you start to collect data. You would never know what exact features you want and how much data is needed. Hence, you go for…
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1.6. Nearest Neighbors
sklearn.neighbors provides functionality for unsupervised and supervised neighbors-based learning methods. Unsupervised nearest neighbors is the foundation of many other learning methods, notably m......
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In-Depth: Manifold Learning
We have seen how principal component analysis (PCA) can be used in the dimensionality reduction task—reducing the number of features of a dataset while maintaining the essential relationships between ...
Read more at Python Data Science Handbook
The Ultimate Scikit-Learn Guide
Part 5: An introduction to spectral biclustering algorithm. Welcome back Machine Learning folks! Another week, another Scikit-Learn example to have a look at. In this episode, we are having a look at...
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Dimensionality Reduction For Dummies — Part 3: Connect The Dots
An intuitive solution to PCA using Eigenvalue Decomposition.
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Master Dimensionality Reduction with these 5 Must-Know Applications of Singular Value…
Singular Value Decomposition is a common dimensionality reduction technique. This article explores the applications of SVD and the different ways of implementing SVD in Python
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Isotonic Regression is THE Coolest Machine-Learning Model You Might Not Have Heard Of
The term “ Isotonic” originates from the Greek root words “ iso” and “ tonos.” The root “ iso” isn’t just a file format, it actually means equal. “ Tonos,” on the other hand, means to stretch. The…
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A Complete Guide On Dimensionality Reduction
Do you know, the interest users are generating 2.5 quintillion bytes of data per day? well the data can be from anywhere. Suppose the data collected from Internet based applications are generating…
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Euclidean and Manhattan distance metrics in Machine Learning.
Many of the Supervised and Unsupervised machine learning models such as K-Nearest Neighbor and K-Means depend upon the distance between two data points to predict the output. Therefore, the metric we…...
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Let’s learn about Dimensionality Reduction
For example:- We have data in spreadsheet format and we have vast amounts of variables (age, name, sex, Id, and so on..). In a simple way “The number of input variables or features for a dataset is…
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