CompTIA IT Fundamentals Certification certificate
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1) KNN measures the distance between query points and training samples to classify new objects based on the majority category of its K nearest neighbors.
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This presentation covers Decision Tree as a supervised machine learning technique, talking about Information Gain method and Gini Index method with their related Algorithms.
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YouTube Link: https://youtu.be/rZjhId0VkuY
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This Edureka PPT on 'Dictionary In Python' will help you understand the concept of dictionary, why and how we can use dictionary in python and various operations that we can perform on a dictionary. Below are the topics covered in this PPT:
What Is A Dictionary In Python?
Why Use A Python Dictionary?
Lists vs Dictionary
How To Implement A Dictionary In Python?
Operations In Python Dictionary
Use Case - Nested Dictionary
Python Tutorial Playlist: https://goo.gl/WsBpKe
Blog Series: http://bit.ly/2sqmP4s
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
trees in data structure
Tree and graph data structures are used to represent hierarchical data. A tree has nodes connected by edges in a parent-child relationship where each node can have zero or more children but only one parent, forming a rooted tree. Common tree types include binary trees where each node has at most two children, binary search trees where the left child is less than the parent which is less than the right child, and B-trees where nodes can have multiple children. Trees are traversed using in-order, pre-order and post-order algorithms.
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The document discusses sequential pattern mining, which involves finding frequently occurring ordered sequences or subsequences in sequence databases. It covers key concepts like sequential patterns, sequence databases, support count, and subsequences. It also describes several algorithms for sequential pattern mining, including GSP (Generalized Sequential Patterns) which uses a candidate generation and test approach, SPADE which works on a vertical data format, and PrefixSpan which employs a prefix-projected sequential pattern growth approach without candidate generation.
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This document provides an introduction to data mining, including its motivation, definition, applications, and key concepts. It discusses how the explosion of data has created a need for data mining to extract useful knowledge from large databases. Data mining involves techniques from machine learning, statistics, database technology, and information visualization to discover patterns in data. It can be used for applications like market analysis, risk assessment, and fraud detection. The document outlines the typical steps of the data mining process and different data mining functionalities, such as classification, clustering, and association rule mining. It also notes that not all patterns discovered will be interesting to users.
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Pruning is the process of removing branches or nodes from a decision tree to simplify it and reduce overfitting. Some key points about pruning:
- Pruning reduces the complexity of the decision tree to avoid overfitting to the training data.
- It is done to improve the accuracy of the model on new unseen data by removing noisy or unstable parts of the tree.
- Common pruning techniques include pre-pruning, cost-complexity pruning, reduced error pruning etc.
- The goal of pruning is to find a tree with optimal complexity that balances bias and variance for best generalization on new data.
To answer your question - tree based models and linear models each have their own advantages in different situations:
Data Structure and Algorithms Arrays
This slide explains some basics concepts of array and operation performed on arrays like searching and sorting.
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NumPy.pptx
NumPy is a Python package that provides multidimensional array objects and processing functions. It is useful for scientific computing as NumPy arrays use less memory than Python lists and allow specifying data types. NumPy has been available since 2005 and allows creating arrays with 1, 2, or 3 dimensions. Key attributes of NumPy arrays include shape, size, and ndim, which provide information on the array dimensions and number of elements.
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CompTIA IT Fundamentals Certification certificate
- 1. Luis Angel Lopez COMP001020827226 February 28, 2015