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#XGBoost ~ Extreme Gradient Boosting, a powerful Machine Learning Algorithm. This algorithm follows the Ensemble learning method which sequentially adds weak learners one after the other to produce a reliable and accurate predictive model. #towardsdatascience #machinelearning

Do you want to understand how XGBoost works? Check this very informative post by 🔥 Matt Dancho 🔥.

Understanding XGBoost. #ML #datascience #AI

XGBoost is now the go-to number 1 must-have algorithm in my data science toolkit. But for years, I had no clue what I was doing. In 3 minutes, I’ll share 3 months of research (business case included). Let’s go: 1. XGBoost, which stands for Extreme Gradient Boosting, is an advanced implementation of the gradient boosting machine (GBM) algorithm. It was developed to optimize both computational speed and model performance. 2. Gradient Boosting Machine (GBM): GBMs are an ensemble approach that combines multiple weak learners (typically decision trees) to create a strong predictive model. 3. Difference between GBM and Random Forest: Random Forest also uses decision trees. However, the difference is how the trees are developed. GBM sequentially adds weak learners (shorter trees), where each one corrects its predecessor. RF constructs strong learners (large trees) in parallel using Bootstrap Aggregation (Bagging). 4. Performance: XGBoost is an ultra-fast implementation of GBM that includes high-efficiency, scalability, ability to handle sparse data, parallel learning, and regularization to reduce over-fitting. XGBoost tends to be more efficient than Random Forest and traditional GBM, and often provides better performance. This is why I like it so much. 5. Business Use Cases: I use XGBoost in many business cases. Let’s start with the one that made the most impact, a $12,000,000 sales increase. Lead scoring. In 2 years of developing a Lead Scoring Model that made my company $12,000,000, I used a number of different algorithms. I started with the most basic- Logistic Regression for classification probability of a customer purchasing. And over time, improved it with better algorithms, better and more complex features. The final iteration had XGBoost as a key model in the algorithm. 6. Business Use Case (Time Series): I later discovered that I could use XGBoost on time series for forecasting sales demand at the product level. This was a major improvement over less-scalable techniques (ARIMA, ETS) that had to be run iteratively on every product. We had 12,000+ products. XGBoost cut training times from 3 days to about 4 minutes. One thing to watch out for is that because it’s tree-based, XGBoost cannot predict beyond the maximum or below the minimum in a dataset. So differencing may be required. There you have it- my top 6 concepts on XGBoost. The next problem you'll face is how to apply data science to business. I'd like to help. I’ve spent 100 hours consolidating my learnings into a free 5-day course, How to Solve Business Problems with Data Science. It comes with: 300+ lines of R and Python code  5 bonus trainings 2 systematic frameworks 1 complete roadmap to avoid mistakes and start solving business problems with data science, TODAY. 👉 Here it is for free: https://lnkd.in/e_EkiuFD

XGBoost is now my go-to number 1 must-have algorithm in my data science toolkit. But for years, I had no clue what I was doing. In 3 minutes, I’ll share 3 months of research (business case included). Let’s go: 1. XGBoost, which stands for Extreme Gradient Boosting, is an advanced implementation of the gradient boosting machine (GBM) algorithm. It was developed to optimize both computational speed and model performance. 2. Gradient Boosting Machine (GBM): GBMs are an ensemble approach that combines multiple weak learners (typically decision trees) to create a strong predictive model. 3. Difference between GBM and Random Forest: Random Forest also uses decision trees. However, the difference is how the trees are developed. GBM sequentially adds weak learners (shorter trees), where each one corrects its predecessor. RF constructs strong learners (large trees) in parallel using Bootstrap Aggregation (Bagging). 4. Performance: XGBoost is an ultra-fast implementation of GBM that includes high-efficiency, scalability, ability to handle sparse data, parallel learning, and regularization to reduce over-fitting. XGBoost tends to be more efficient than Random Forest and traditional GBM, and often provides better performance. This is why I like it so much. 5. Business Use Cases: I use XGBoost in many business cases. Let’s start with the one that made the most impact, a $12,000,000 sales increase. Lead scoring. In 2 years of developing a Lead Scoring Model that made my company $12,000,000, I used a number of different algorithms. I started with the most basic- Logistic Regression for classification probability of a customer purchasing. And over time, improved it with better algorithms, better and more complex features. The final iteration had XGBoost as a key model in the algorithm. 6. Business Use Case (Time Series): I later discovered that I could use XGBoost on time series for forecasting sales demand at the product level. This was a major improvement over less-scalable techniques (ARIMA, ETS) that had to be run iteratively on every product. We had 12,000+ products. XGBoost cut training times from 3 days to about 4 minutes. One thing to watch out for is that because it’s tree-based, XGBoost cannot predict beyond the maximum or below the minimum in a dataset. So differencing may be required. === Need help improving your data science skills? I'd like to help. Here's how: 👉 My Free 10 Skills Webinar: I put together a free on-demand workshop that covers the 10 skills that helped me make the transition to Data Scientist: https://buff.ly/3P9Js85 👉 Learn ChatGPT for 10X Faster DS Projects: I have a live workshop where I'll share how to use ChatGPT for Data Science (so you can complete projects 10X faster): https://buff.ly/3Tozrqm If you like this post, please reshare ♻️ it so others can get value (follow me, 🔥 Matt Dancho 🔥 for more data science concepts).

Understanding XGBoost: A Powerful Machine Learning Tool XGBoost, short for eXtreme Gradient Boosting, is a powerful and widely popular machine learning algorithm belonging to the ensemble learning category. It's known for its efficiency, accuracy, and flexibility, making it a go-to choice for various tasks, including: Regression: Predicting continuous values, like housing prices or stock prices. Classification: Categorizing data points, like spam or non-spam emails. Ranking: Ordering items based on their relevance or importance, like search engine results. #machinelearning #dataengineering #dataanalytics

Ensemble Methods - Foundations and Algorithms: Enhancing Predictive Power Ensemble methods are powerful techniques in machine learning that combine multiple models to produce a superior result. Here's a breakdown of the foundations and algorithms involved: Foundations of Ensemble Methods: 📌 Diversity of Models:   Combining different models helps to capture various patterns in the data, reducing errors and improving accuracy. 📌 Reduction of Overfitting:   Ensemble methods help mitigate overfitting by averaging out biases, leading to more robust predictions. 📌 Improved Performance:   Leveraging the strengths of multiple models enhances predictive performance and stability. --- Key Algorithms in Ensemble Methods: 📊 Bagging (Bootstrap Aggregating):   Involves training multiple versions of a model on different subsets of the data and averaging their predictions. Example: Random Forest. 📊 Boosting:   Sequentially trains models, each trying to correct the errors of its predecessor. Example: AdaBoost, Gradient Boosting. 📊 Stacking (Stacked Generalization):   Combines the predictions of several models using a meta-model. This approach can leverage the strengths of various learning algorithms. 📊 Voting:   Aggregates the predictions of multiple models by taking the majority vote (for classification) or averaging (for regression). --- Applications and Benefits: 📌 Versatility:   Applicable to a wide range of problems including classification, regression, and anomaly detection. 📌 Accuracy:   Often achieves higher accuracy compared to individual models. 📌 Robustness:   Offers more reliable predictions by reducing the risk of model-specific errors. Harnessing ensemble methods can significantly enhance your machine learning models' performance and reliability. Ready to elevate your predictive analytics? Credit : Zhi-Hua-Zhou ✅ Subscribe to our Youtube channel: https://lnkd.in/gD54ZjUh ✅ P.S. Want to Upskill your Data Science workforce? Check out our course catalog for corporate training, https://lnkd.in/dYipv_Qm ❤️ #datasciencecommunity --------------------------------- 💥 I am Digvijay singh. 🔺Supporting you to Upskill in DataScience skills. 🔺Providing Corporate IT training, consulting, mentoring with a team of Sr. Data Science Certified Experts. ☎️ CONTACT: +1 (515) 309-7846 (or) Email - [email protected] ✅ WhatsApp us for more info: https://wa.me/15153097846 More information: 🌍 Website: https://www.zarantech.com/ 🌍 Instagram: https://lnkd.in/gYJct6D 🌍 Linkedin: https://lnkd.in/gWjHpzm 🌍 Facebook: https://lnkd.in/gprZE9S 🌍 Quora: https://lnkd.in/g2Fy2v39 Like this post? Want to see more? 🔔Ring it on my Profile 🔹Follow hashtag#DataScienceLearnerCommunity 🔹Follow our Linkedin Learner community, https://lnkd.in/gMZw7-MN #datascience #datasciencecommunity #ai #training #python #sql #al #ml

Metrics in Data Science 1. A confusion matrix is a tool often used in machine learning to visualize the performance of a classification model. It's a table that allows you to compare the model's predictions against the actual values. 2. Correct Predicitions: True Positives (TP): These are cases in which the model correctly predicts the positive class. True Negatives (TN): These are cases in which the model correctly predicts the negative class. 3. Model Errors: False Positives (FP, Type I Error): These are cases in which the model incorrectly predicts the positive class. False Negatives (FN, Type II): These are cases in which the model incorrectly predicts the negative class. 4. My Big Mistake: In machine learning we're taught to optimize for model performance. I listened. I said OK, let's optimize for F1 Score. That's the gold standard right? 5. The Problem with F1 Score: The problem with F1 is that it weights False Positives (Type 1) and False Negatives (Type 2) Errors equally. But in business this is RARELY the case. False Negatives are normally 10X to 100X more costly to a business. Let me explain with an example: Netflix. 6. Why minimizing False Positives is worth LESS: If a predictive model (False Positive) incorrectly predicts a customer is going to leave, and Netflix decides to send them preventative actions like a discounted deal. The customer takes it and saves 10%. But they would have stayed anyway. Over a year that costs Netflix $12. 7. Why minimizing False Negatives is worth MORE: Now on the flip side, if Netflix's model incorrectly classifies some one that is on the edge of leaving as predicted to stay, Netflix does nothing. That customer leaves. Over a year that costs Netflix $120. And over the lifetime that could be $500+. 8. What they don't teach you: Expected Value (EV). I'll have a post on that soon. It's how you optimize Machine Learning models for $$$ instead of F1. === Ready to learn Data Science for Business? I put together a free on-demand workshop that covers the 10 skills that helped me make the transition to Data Scientist: https://lnkd.in/gbEBVf5f And if you'd like to speed it up, I have a live workshop where I'll share how to use ChatGPT for Data Science: https://lnkd.in/gCvh6UAy If you like this post, please reshare ♻️ it so others can get value. Credit >>🔥 Matt Dancho 🔥 #metrics #matrix #datascience #datasciencejourney

K-Nearest Neighbors(KNN) K-Nearest Neighbors (KNN) is a simple and versatile supervised machine learning algorithm used for both classification and regression tasks. It's a non-parametric(which means it does not make any assumption on underlying data), and lazy learning algorithm, meaning it does not learn from the training set immediately instead it stores the dataset and at the time of classification, it performs an action on the dataset. KNN algorithm at the training phase just stores the dataset and when it gets new data, then it classifies that data into a category that is much similar to the new data. ➡️When a new data point needs to be classified or predicted, the algorithm calculates the distance between the new data point and all the data points in the training set. Common distance metrics include Euclidean distance, Manhattan distance, or others depending on the application. ➡️The KNN algorithm then identifies the K nearest neighbors to the new data point based on the calculated distances. "K" is a user-defined parameter that represents the number of neighbors to consider. ⚫For classification:    -If K is an odd number, it assigns the class label that is most common among the K neighbors to the new data point.    -If K is an even number, it may use voting mechanisms like weighted voting to break ties.    ⚫For regression:         -It takes the average (or weighted average) of the target values of the K neighbors and assigns this average as the predicted value for the new data point. ➡️The choice of K is crucial and depends on the specific dataset and problem. Smaller values of K make the algorithm more sensitive to noise, while larger values make it smoother but may over fit the model. ➡️It's important to normalize or standardize features to ensure that all features contribute equally to the distance calculation, especially if features have different scales. 📒In summary, KNN classifies or predicts new data points based on the majority class or average value of their K nearest neighbors in the training set. The algorithm's simplicity makes it easy to understand and implement, but its efficiency can be a concern for large datasets. 🌟The code demonstrates the implementation of a K-nearest neighbors (KNN) algorithm on a car evaluation dataset. It begins by loading and preprocessing the dataset, including label encoding for categorical columns. The implementation includes both a custom KNN classifier and scikit-learn's KNeighborsClassifier. The scikit-learn model achieves better accuracy, providing a detailed classification report. . . . . . . . #machinelearning #dataanalytics #machinelearningengineer #python #knn #distance #predictivemodeling #pythoncoding #pythonlearning #pythonfordatascience #pythonprogrammer #python #artificialintelligence #ml #pythonprogramming #machinelearningalgorithms #machinelearningtraining #machinelearningmodels #statistics #sklearn #classification

Here are some essential data science concepts from A to Z. Let's dive in! 🏊♂️ A - Algorithm: A set of rules or instructions that solve problems or perform tasks in data science. 📝 B - Big Data: Large, complex datasets that require specialized tools and techniques to process and analyze. 💾 C - Clustering: Grouping similar data points based on specific characteristics. 🧵 D - Data Cleaning: Identifying and correcting errors or inconsistencies in a dataset. 🚧 E - Exploratory Data Analysis (EDA): Analyzing and visualizing data to understand patterns and relationships. 📊 F - Feature Engineering: Creating new features or variables from existing data to improve model performance. ➡️ G - Gradient Descent: An optimization algorithm that minimizes model error by adjusting parameters. ⏳ H - Hypothesis Testing: Statistical technique for testing the validity of a hypothesis on sample data. 💭 I - Imputation: Filling in missing values in a dataset using statistical methods. 🔍 J - Joint Probability: The probability of two or more events occurring together. 🔗 K - K-Means Clustering: A popular clustering algorithm that partitions data into K clusters based on similarity. 🌊 L - Linear Regression: A statistical method modeling the relationship between a dependent variable and one or more independent variables. 📈 M - Machine Learning: A subset of AI that uses algorithms to learn patterns and make predictions from data. 🤖 N - Normal Distribution: A symmetrical bell-shaped distribution commonly used in statistical analysis. 🌊 O - Outlier Detection: Identifying and removing data points significantly different from the rest of the dataset. 🚫 P - Precision and Recall: Evaluation metrics for assessing classification model performance. ✅🔍 Q - Quantitative Analysis: Analyzing numerical data to draw conclusions and make decisions. 💼 R - Random Forest: An ensemble learning algorithm that builds multiple decision trees to improve prediction accuracy. 🌳 S - Support Vector Machine (SVM): A supervised learning algorithm used for classification and regression tasks. 🎯 T - Time Series Analysis: A statistical technique analyzing and forecasting time-dependent data. ⏰ U - Unsupervised Learning: Machine learning without labeled outputs, discovering patterns in data. 🌐 V - Validation Set: A subset of data used to evaluate model performance during training. 🔄 W - Web Scraping: Extracting data from websites for analysis and visualization. 🛠️ X - XGBoost: An optimized gradient boosting algorithm widely used in machine learning competitions. 🚀 Y - Yield Curve Analysis: Studying the relationship between interest rates and the maturity of fixed-income securities. 📈 Z - Z-Score: A standardized score representing the number of standard deviations a data point is from the mean. 📊 Keep learning and growing! 🌱🚀 follow 🤝 like 👍 comment 💬 share ♻️ follow Santhosh Tirunahari for more such informative content. #dataanalysis #datascience #ml #ai