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𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗶𝗻 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 What is Feature Distribution? Feature Distribution refers to how values of a particular feature (or variable) are spread or distributed in a dataset. 𝗞𝗲𝘆 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀: ↳ Normal Distribution: Also known as Gaussian distribution, where most values cluster around the mean. ↳ Skewness: Indicates asymmetry in the distribution. Positive skewness means the tail is on the right side, while negative skewness indicates a left-sided tail. ↳ Kurtosis: Measures the “tailedness” of the distribution. High kurtosis means more outliers. 𝗥𝗼𝗹𝗲 𝗶𝗻 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴: ↳ Feature Scaling: Normalize or standardize features to handle differences in distributions before training models like SVM or Logistic Regression. ↳ Handling Outliers: Distributions with high kurtosis require special attention to outliers, which can significantly affect the model’s performance. ↳ Skewed Distributions: For models like Linear Regression, handling skewness through transformations (e.g., log or square root) can improve predictions. 𝗧𝘆𝗽𝗲𝘀 𝗼𝗳 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀: ↳ Uniform Distribution: All values occur with equal probability. ↳ Binomial Distribution: Deals with binary outcomes, like success/failure scenarios. ↳ Exponential Distribution: Useful for modeling time until an event occurs, such as the time between failures in a system. 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲𝘀 𝗼𝗳 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻: ↳ Helps select appropriate feature engineering techniques (e.g., scaling, transformations). ↳ Provides insight into data balance, outliers, and anomalies. ↳ Enhances the interpretability of model performance. 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗶𝗻 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴: ↳ Multicollinearity: Highly correlated features can distort predictions. ↳ Imbalanced Classes: In classification tasks, skewed distributions of target labels can result in poor model performance on minority classes. 𝗛𝗼𝘄 𝗶𝘁 𝗪𝗼𝗿𝗸𝘀: 1. Explore the Distribution: Use histograms or density plots to visualize the distribution of each feature. 2. Identify Outliers: Check for outliers that may affect model accuracy. 3. Transform Data: Apply log, sqrt, or box-cox transformations to correct skewness if necessary. 4. Feature Engineering: Based on distribution insights, decide whether scaling, binning, or other techniques are required for optimal model performance. --- 📕 400+ 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: https://lnkd.in/gv9yvfdd 📘 𝗣𝗿𝗲𝗺𝗶𝘂𝗺 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 : https://lnkd.in/gPrWQ8is 📙 𝗣𝘆𝘁𝗵𝗼𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗟𝗶𝗯𝗿𝗮𝗿𝘆: https://lnkd.in/gHSDtsmA 📗 45+ 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝘀 𝗕𝗼𝗼𝗸𝘀 𝗘𝘃𝗲𝗿𝘆 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝗡𝗲𝗲𝗱𝘀: https://lnkd.in/ghBXQfPc

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Hello AI Enthusiasts ! We are thrilled to announce an exciting online event “Future-Proof Yourself in the Generative AI Era” organized by the Btech CSE -AI&DS Department (Infomaniac Club).  **Date & Time** : Saturday, 12th October 2024 ,11:00 AM-12:30 PM  **Speaker** : Mr. Aroh Shukla,Microsoft and Mr. Sanjeev Venkatram Regional Microsoft Cloud Architect Lead - Asia Pacific | Ex-Microsoft Microsoft MVP Alumni & MCT | C# Corner MVP  Key topics include : 1.Current Job Market Situation 2.Jobs Impacted by Generative AI 3.Hiring and Firing Trends Across All Sectors 4.The Impact of Generative AI on the Workforce 5.Strategies for Future-Proofing Your Career  Registration link:[ https://lu.ma/c8xoxv7a ] Join us to gain valuable insights on how to navigate and prepare for the rapidly changing landscape of the job market, especially with the rise of Generative AI. **Faculty Coordinators:** Ms. Jyoti Dahiya, Ms. Shreya **Student Coordinators** Anna (2nd year) Lakshay (2nd year) Looking forward to seeing

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Interested can learn. Hi Prof.(Dr.) B.K., It's great connecting with you. How have you been? Well, I am Shruti Singhal, associated with Punjab Technical University as an Assistant Professor and also an educational website compgeek.co.in CompGeek.co.in strives to enhance computer science education for all, irrespective of distance, time constraints, or financial barriers. Share with aspiring students aiming for success in life. hashtag #CompGeek

𝗥𝗮𝗻𝗱𝗼𝗺 𝗙𝗼𝗿𝗲𝘀𝘁: 🌳 What makes Random Forests so powerful and accurate? How can they outperform individual Decision Trees? 🤔 ↓↓↓ 𝐑𝐚𝐧𝐝𝐨𝐦 𝐅𝐨𝐫𝐞𝐬𝐭 𝐌𝐨𝐝𝐞𝐥 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐞𝐝: A Random Forest is an ensemble learning method that combines multiple Decision Trees to improve model accuracy and robustness. It reduces overfitting, increases stability, and is capable of handling large datasets with higher dimensions. Here’s a breakdown: 👇 💠 𝐂𝐫𝐞𝐚𝐭𝐢𝐧𝐠 𝐌𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐓𝐫𝐞𝐞𝐬 Each Decision Tree in the Random Forest is trained on a different subset of the data (with replacement). This technique is known as 𝐁𝐚𝐠𝐠𝐢𝐧𝐠 (Bootstrap Aggregating). By combining the predictions of multiple trees, the Random Forest algorithm reduces variance and improves accuracy. --- 💠 𝐑𝐚𝐧𝐝𝐨𝐦𝐧𝐞𝐬𝐬 𝐢𝐧 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐒𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧 When building each tree, only a random subset of features is considered for splitting at each node. This ensures that the trees are not too similar, further reducing overfitting and making the forest more robust against noisy data. --- 💠 𝐌𝐚𝐣𝐨𝐫𝐢𝐭𝐲 𝐕𝐨𝐭𝐢𝐧𝐠 For classification problems, the Random Forest aggregates the predictions of all its trees through majority voting. In regression tasks, it averages the predictions of the trees, providing a more accurate and stable output. --- 💠 𝐀𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞𝐬 𝐨𝐟 𝐑𝐚𝐧𝐝𝐨𝐦 𝐅𝐨𝐫𝐞𝐬𝐭 ↳ Handles both classification and regression problems. ↳ Provides a good indicator of feature importance. ↳ Resistant to overfitting, especially with a large number of trees. ↳ Robust to missing data. --- 💠 𝐃𝐫𝐚𝐰𝐛𝐚𝐜𝐤𝐬 𝐨𝐟 𝐑𝐚𝐧𝐝𝐨𝐦 𝐅𝐨𝐫𝐞𝐬𝐭 ↳ Computationally expensive with large datasets. ↳ Slower to predict compared to simpler models. ↳ Less interpretable than a single decision tree. --- 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐑𝐚𝐧𝐝𝐨𝐦 𝐅𝐨𝐫𝐞𝐬𝐭: ↳ Hands-On Machine Learning with Scikit-Learn: https://lnkd.in/gWyrm5gc ↳ Random Forest Algorithm from Scratch: https://lnkd.in/ggVuYky4 --- 📕 400+ 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: https://lnkd.in/gv9yvfdd 📘 𝗣𝗿𝗲𝗺𝗶𝘂𝗺 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 : https://lnkd.in/gPrWQ8is 📙 𝗣𝘆𝘁𝗵𝗼𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗟𝗶𝗯𝗿𝗮𝗿𝘆: https://lnkd.in/gHSDtsmA 📗 45+ 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝘀 𝗕𝗼𝗼𝗸𝘀 𝗘𝘃𝗲𝗿𝘆 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝗡𝗲𝗲𝗱𝘀: https://lnkd.in/ghBXQfPc