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[shireeshatib]

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[expertlytics]

——————————————————————— OVERFITTING and UNDER-FITTING ——————————————————————— The concept of overfitting refers to creating a model that doesn't generalize to your model. In other words, if your model overfits your data, that means it's learned your data too much - it's essentially memorized it. This might not seem like it would be a problem at first, but a model that's just "memorized" your data is one that's going to perform poorly on new, unobserved data. - Underfitting, on the other hand, is when your model is too generalized to your data. This model will also perform poorly on new unobserved data. This usually means we should increase the number of considered features, which will expand the hypothesis space. ———————————————————————— - #data #datascience #datascientist #datavisualization #dataviz #machinelearning #artificialintelligence #machinelearningalgorithms #algorithm #engineering #engineer #math #mathematics #statistics #studygram #learn #study #visualization #simulation #science #mathconcepts #datascienceweekend #overfitting #underfitted #trainingset #testingset #validation #predictionerror (at United States)

[kaushalseo1-blog]

[inventive9]

Anatomy of a Data Scientist | Data Science https://youtu.be/p2yJrNuk53c #inventive9 #datascience #sciencedata #datasciencetraining #datasciencejobs #datasciences #datascienceeducation #datasciencecourse #datasciencelearning #womenindatascience #datascienceworkshop #datascienceenthusiast #datasciencememes #datasciencebootcamp #datascienceindonesia #datasciencewithpython #datascienceacademy #datasciencenigeria #datasciencecareers #learndatascience #datascienceenthusiasts #datascienceweekend #datascienceindia #datasciencecourses #datasciencelearn #datascienceonlinetraining #datascienceuy #datasciencestudent #datasciencecareer #datascienceid https://www.instagram.com/p/CR3bY7VrH54/?utm_medium=tumblr

[clearlyautomaticanchor]

Machine learning 😌 . . . . . . . #programming #coding #programmer #programminglife #coder #javascript #java #php #programmers #programmings #machinelearning #machinelearningalgorithms #ai #artificialintelligence #datascience #deeplearning #machinelearningtools #data #bigdata #technology #datascience #bigdata #machinelearning #technology #data #datascientist #datascienceweekend #ai #datascienceenthusiasts #datasciencenigeria

[asongsoekamti]

Gilaaaaak serangan fajar bung, banyak banget pertanyaan dalam sesi ku ngomong. 😂 btw, great experience... semoga bisa diundang acara-acara seperti ini lagi... . "How a thief steal your data over network" - Andreas Bernhard, CCNA, CCSA . UII Jogjakarta @ International Conference Data Science, 03 Dec 2016 . . #icds #datascience #datascienceweekend #networksecurity #datasecurity #bigdatasecurity #jogjakarta #universitasislamindonesia (at Universitas Islam Indonesia)

[expertlytics]

——————————————————————— The bias-variance tradeoff is a central problem in supervised learning. Ideally, one wants to choose a model that both accurately capture the regularities in its training data, but also generalizes well to unseen data. Unfortunately, it is typically impossible to do both simultaneously. High-variance learning methods may be able to represent their training set well but are at risk of overfitting to noisy or unrepresentative training data. In contrast, algorithms with high bias typically produce simpler models that don't tend to overfit but may underfit their training data, failing to capture important regularities. Models with low bias are usually more complex (e.g. higher-order regression polynomials), enabling them to represent the training set more accurately. In the process, however, they may also represent a large noise component in the training set, making their predictions less accurate - despite their added complexity. In contrast, models with higher bias tend to be relatively simple (low-order or even linear regression polynomials) but may produce lower variance predictions when applied beyond the training set. ———————————————————————— - #data #datascience #datascientist #datavisualization #dataviz #machinelearning #artificialintelligence #machinelearningalgorithms #algorithm #engineering #engineer #math #mathematics #statistics #studygram #learn #study #visualization #simulation #science #mathconcepts #datascienceweekend #bias #variance #tradeoff #error #estimationerror (at United States)

[expertlytics]

—————————————————————— Entropy, as it relates to machine learning, is a measure of the randomness in the information being processed. ... Flipping a coin is an example of an action that provides information that is random. For a coin that has no affinity for 'heads' or 'tails', the outcome of any number of tosses is difficult to predict. - In information theory, entropy is a measure of the uncertainty associated with a random variable. The term by itself in this context usually refers to the Shannon entropy, which quantifies, in the sense of an expected value, the information contained in a message, usually in units such as bits. - Entropy and decision Trees - ID3 uses Entropy and Information Gain to construct a decision tree. Entropy. A decision tree is built top-down from a root node and involves partitioning the data into subsets that contain instances with similar values (homogenous). ID3 algorithm uses entropy to calculate the homogeneity of a sample. —————————————————————— - #data #datascience #datascientist #datavisualization #dataviz #machinelearning #artificialintelligence #machinelearningalgorithms #algorithm #engineering #engineer #math #mathematics #statistics #studygram #learn #study #visualization #illustrationstudy #science #mathconcepts #datascienceweekend #databases #datacenter #tech #entropy #informationgain

[expertlytics]

—————————————————————— A statistical method used to determine a line of best fit by minimizing the sum of squares created by a mathematical function. A "square" is determined by squaring the distance between a data point and the regression line. —————————————————————— - #data #datascience #datascientist #datavisualization #dataviz #machinelearning #artificialintelligence #machinelearningalgorithms #algorithm #engineering #engineer #math #mathematics #statistics #studygram #learn #study #visualization #illustrationstudy #science #mathconcepts #datascienceweekend #databases #datacenter #tech #leastsquares #regression

[expertlytics]

—————————————————————— In statistics, overfitting is the production of an analysis that corresponds too closely or exactly to a particular set of data, and may therefore fail to fit additional data or predict future observations reliably. An overfitted model is a statistical model that contains more parameters than can be justified by the data. The essence of overfitting is to have unknowingly extracted some of the residual variation (i.e. the noise) as if that variation represented underlying model structure. - Underfitting occurs when a statistical model cannot adequately capture the underlying structure of the data. An underfitted model is a model where some parameters or terms that would appear in a correctly specified model are missing. Underfitting would occur, for example, when fitting a linear model to non-linear data. Such a model will tend to have poor predictive performance. Overfitting and underfitting can occur in machine learning, in particular. In machine learning, the phenomena are sometimes called "overtraining" and "undertraining". —————————————————————— - #data #datascience #datascientist #datavisualization #dataviz #machinelearning #artificialintelligence #machinelearningalgorithms #algorithm #engineering #engineer #math #mathematics #statistics #studygram #learn #study #visualization #illustrationstudy #science #mathconcepts #datascienceweekend #tech #bias #biasvariancetradeoff #variance #tradeoff #overfitting #underfitting #modelcomplexity #error

[kaushalseo1-blog]

[expertlytics]

——————————————————————— K-means is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. ... The main idea is to define k centroids, one for each cluster. These centroids shoud be placed in a cunning way because of different location causes different result. - The k-means clustering algorithm attempts to split a given anonymous data set (a set containing no information as to class identity) into a fixed number (k) of clusters. Initially k number of so called centroids are chosen. A centroid is a data point (imaginary or real) at the center of a cluster. - ——————————————————————— The concept of overfitting refers to creating a model that doesn't generalize to your model. In other words, if your model overfits your data, that means it's learned your data too much - it's essentially memorized it. This might not seem like it would be a problem at first, but a model that's just "memorized" your data is one that's going to perform poorly on new, unobserved data. - Underfitting, on the other hand, is when your model is too generalized to your data. This model will also perform poorly on new unobserved data. This usually means we should increase the number of considered features, which will expand the hypothesis space. ———————————————————————— - #data #datascience #datascientist #datavisualization #dataviz #machinelearning #artificialintelligence #machinelearningalgorithms #algorithm #engineering #kmeans #math #mathematics #statistics #studygram #learn #study #visualization #simulation #science #computerscience #mathconcepts #datascienceweekend #research #economy (at United States)

[expertlytics]

——————————————————————— Analysis of variance is a collection of statistical models and their associated procedures used to analyze the differences among group means. ———————————————————————— - #data #datascience #datascientist #datavisualization #dataviz #machinelearning #artificialintelligence #machinelearningalgorithms #algorithm #engineering #engineer #math #mathematics #statistics #studygram #learn #study #visualization #illustrationstudy #science #mathconcepts #datascienceweekend #databases #datacenter #tech #anova #variance (at United States)

[expertlytics]

—————————————————————— A self-organizing map (SOM) is a clustering technique that helps you uncover categories in large datasets, such as to find customer profiles based on a list of past purchases. It is a special breed of unsupervised neural networks, where neurons (also called nodes or reference vectors) are arranged in a single, 2-dimensional grid, which can take the shape of either rectangles or hexagons. - Through multiple iterations, neurons on the grid will gradually coalesce around areas with high density of data points. Hence, areas with many neurons might reflect underlying clusters in the data. As the neurons move, they inadvertently bend and twist the grid to more closely reflect the overall topological shape of our data. —————————————————————— - #data #datascience #datascientist #datavisualization #dataviz #machinelearning #artificialintelligence #machinelearningalgorithms #algorithm #engineering #engineer #math #mathematics #statistics #studygram #learn #study #visualization #illustrationstudy #science #mathconcepts #datascienceweekend #databases #datacenter #tech #som #selforginizing #deeplearning

[expertlytics]

—————————————————————— A convex optimization problem is a problem where all of the constraints are convex functions, and the objective is a convex function if minimizing, or a concave function if maximizing. Linear functions are convex, so linear programming problems are convex problems. —————————————————————— - #data #datascience #datascientist #datavisualization #dataviz #machinelearning #artificialintelligence #machinelearningalgorithms #algorithm #engineering #engineer #math #mathematics #statistics #studygram #learn #study #visualization #illustrationstudy #science #mathconcepts #datascienceweekend #databases #datacenter #tech #optimization #convexoptimization #convex

[expertlytics]

—————————————————————— BFS and DFS are graph traversal/searching algorithms. Since, a graph can be used to represent a large number of real life problems such as road networks, computer networks, social networks such as facebook etc., BFS/DFS can be applied to solve a myriad of real life problems. - GPS Navigation systems: Navigation systems such as the Google Maps, which can give directions to reach from one place to another use shortest path algorithms. They take your location to be the source node and your destination as the destination node on the graph. - Computer Networks: Peer to peer (P2P) applications such as the torrent clients need to locate a file that the client is requesting. This is achieved by applying BFS on the hosts. - Web Crawlers: They can be used to analyze what all sites you can reach by following links on a particular website. - Facebook: It treats each user profile as a node on the graph and two nodes are said to be connected if they are each other's friends. Infact, apply BFS on the facebook graph and you will find that any two people are connected with each other by atmost five nodes in between. To say, that you can reach any random person in the world by traversing 6 nodes. —————————————————————— - #data #datascience #datascientist #datavisualization #dataviz #machinelearning #artificialintelligence #machinelearningalgorithms #algorithm #engineering #engineer #math #mathematics #statistics #studygram #learn #study #visualization #illustrationstudy #science #mathconcepts #datascienceweekend #databases #datacenter #tech #bfs #dfs #graph