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10 Best Artificial Intelligence Tools To Analyze Data Read More:- https://buff.ly/3X2kEkx #artificialintelligence #AItools #analyzedata #dataanalysis #TheTechTrend

Are you interested in incorporating Machine Learning models into your daily business operations? Numerous technologies are now accessible to aid in forecasting the likelihood of a financial contract applicant fulfilling their responsibilities. Typically, institutions do not outright reject applicants failing to meet minimum requirements. Instead, they seek to find a better fit for such applicants by exploring alternative options. Consequently, a decision-management approach that considers the "Next Best Action" becomes imperative. Opting to collaborate with SAS ensures streamlined model deployment for enhanced decision-making across various financial services scenarios. What you'll discover: Techniques for registering and monitoring the performance of analytical models over time. Methods to seamlessly integrate analytics models with business rules for improved decision-making. Harnessing Recommender Systems for determining the "Next Best Action." Join us for a brief session on Tuesday, April 25th, from 2:00 to 2:30 pm EST, and hear insights from Solutions Architect and Data Scientist, Gene Grabowski. Registration Link: http://2.sas.com/6041wZ9jl #modelops #analytics

🗣 "Becoming distracted by the latest in AI or another shiny tool is the easiest way for the data functions to become detached from the rest of the business". Yesterday, I wrote a short piece about the importance of having NEDs on the board with a a background in data & analytics for extra assurance that data functions stay line with business needs. This sparked a conversation on our main LinkedIn page, and so I wanted to ask my network, what are your thoughts❓ #data #analytics #nonexecutivedirectors https://lnkd.in/dqDbxkmv

This computational aspects of information theory helps to perform well in ID3 algorithm task to make the decision tree classification and regression problem solution.

Information Gain (IG) is critical in machine learning and decision tree algorithms, particularly in data classification and 𝐟𝐞𝐚𝐭𝐮𝐫𝐞 𝐬𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧.   It is pivotal in determining the optimal way to split data in decision trees, enabling more effective and accurate decision-making processes.   The primary objective of decision trees is to create a model that can classify data points into different categories or classes based on their features.   To accomplish this, decision trees recursively split the dataset into subsets, aiming to maximize each resulting subset's homogeneity (purity) concerning the target class variable.   IG is a metric to quantify the reduction in uncertainty or randomness in the data after a particular split.   The Algorithmic Flow   ▸Before any splits, the impurity of the entire dataset is measured using metrics like 𝒆𝒏𝒕𝒓𝒐𝒑𝒚 or 𝑮𝒊𝒏𝒊 𝒊𝒎𝒑𝒖𝒓𝒊𝒕𝒚. Higher impurities indicate a mix of different classes within the dataset.   ▸Decision trees evaluate various features and their thresholds to determine the most informative split. The goal is to find the feature and threshold that best separates the data into subsets with lower impurities.   ▸After the split, IG is calculated to measure the reduction in impurity: 𝘐𝘎 = 𝘐𝘯𝘪𝘵𝘪𝘢𝘭 𝘐𝘮𝘱𝘶𝘳𝘪𝘵𝘺 - 𝘞𝘦𝘪𝘨𝘩𝘵𝘦𝘥 𝘈𝘷𝘦𝘳𝘢𝘨𝘦 𝘰𝘧 𝘚𝘶𝘣𝘴𝘦𝘵𝘴' 𝘐𝘮𝘱𝘶𝘳𝘪𝘵𝘪𝘦𝘴 The proportion of data weights each subset's impurity points relative to the original dataset—the weighted average accounts for the size of each subset.   ▸Decision trees repeat this process for all available features and thresholds, calculating IG for each possible split. The split with the highest IG is selected as the best choice.   ▸Splitting and calculating IG is repeated recursively for each subset until a predefined stopping criterion is met. This leads to the creation of a hierarchical decision tree.   IG helps decision trees make intelligent decisions about how to divide the data by quantifying the reduction in uncertainty that each potential split offers.   High IG implies that a split leads to more homogenous subsets, making it a favorable choice for building an accurate classification model.   Image: Author #artificialintelligence #machinelearning #datascience #analytics

Explore the role of Information Gain (IG) in decision tree algorithms: Danny Butvinik explains how IG guides optimal data splits, enhancing accuracy in classification. From measuring impurity to selecting the best split, grasp the algorithmic flow in just a few steps, shown in the illustration below. IG's role is pivotal, quantifying uncertainty reduction and leading to the creation of hierarchical decision trees. #datascience #machinelearning #ai #artificialintelligence

DataGPT Xpress is Live! Signup here: https://lnkd.in/dkeYrta2 "AI-driven tools such as DataGPT's Google Analytics Connector revolutionize the way businesses perform analytics in today's big data era. When users interact with their data in ordinary language, they simplify data analysis, which was previously complex, through the usage of DataGPT's Google Analytics Connector, therefore making it accessible to a larger number of people." Read more about DataGPT Xpress in Analytics Insight® #GA #AIAnalytics #DataGPTXpress

Hello Alex Freberg (aka Alex The Analyst) - many thanks for this YouTube Video: https://lnkd.in/duuY82Fn. I'm thrilled you enjoyed SAS Explore and we were able to broaden your perspective about SAS. #exploreSAS

Exploring the Benefits and Limitations of Dimensionality Reduction Techniques 📣 Exciting news! 🚀 Check out our latest blog post on "Exploring the Benefits and Limitations of Dimensionality Reduction Techniques"! 🧠💡 In the era of big data, dealing with high-dimensional data can be a challenge for data analysts and machine learning practitioners. But fear not! Our new article dives deep into dimensionality reduction techniques that offer a solution by reducing variables while preserving essential information. 📊📉 Discover the benefits of dimensionality reduction, including improved computational efficiency, enhanced visualization, reduced storage requirements, improved model performance, and noise reduction. 🤩🚀 But wait, it's not all rainbows and unicorns. Learn about the limitations too, such as information loss, reduced interpretability, challenges in dealing with the curse of dimensionality, computational complexity, and sensitivity to parameter settings. 🤔💥 Knowledge is power! So read the article now to make informed decisions and effectively leverage dimensionality reduction techniques for extracting meaningful insights from high-dimensional data. 💪🔍 🔗 Read the full blog post here: [Exploring the Benefits and Limitations of Dimensionality Reduction Techniques](https://ift.tt/5RO4Wlj) #DataAnalytics #MachineLearning #DimensionalityReduction #DataInsights https://ift.tt/5RO4Wlj