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𝑯𝒚𝒑𝒆𝒓𝒑𝒂𝒓𝒂𝒎𝒆𝒕𝒆𝒓 𝑻𝒖𝒏𝒊𝒏𝒈 𝒊𝒏 𝑴𝒂𝒄𝒉𝒊𝒏𝒆 𝑳𝒆𝒂𝒓𝒏𝒊𝒏𝒈 Hyperparameter tuning is the process of finding the best set of parameters for a machine learning model. Unlike parameters learned during training, hyperparameters are set beforehand and control the learning process. • 𝐈𝐦𝐩𝐚𝐜𝐭 𝐨𝐟 𝐓𝐮𝐧𝐞𝐝 𝐇𝐲𝐩𝐞𝐫𝐩𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐬 𝐨𝐧 𝐒𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐌𝐋 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 - 1. 𝐋𝐢𝐧𝐞𝐚𝐫 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧 - Tuned Hyperparameter: Regularization strength (L1 or L2 penalty). - Impact: Proper regularization prevents overfitting, leading to better predictions. - Real-World Use Case: Predicting housing prices based on features like area, bedrooms, and location. 2. 𝐋𝐨𝐠𝐢𝐬𝐭𝐢𝐜 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧 - Tuned Hyperparameter: Learning rate or regularization strength. - Impact: Balanced hyperparameters improve classification accuracy. - Real-World Use Case: Identifying spam emails or predicting customer churn. 3. 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐓𝐫𝐞𝐞 - Tuned Hyperparameter: Maximum depth or minimum samples per leaf. - Impact: Pruning controls tree complexity and avoids overfitting. - Real-World Use Case: Detecting fraudulent transactions. 4. 𝐊-𝐍𝐞𝐚𝐫𝐞𝐬𝐭 𝐍𝐞𝐢𝐠𝐡𝐛𝐨𝐫𝐬 (𝐊𝐍𝐍) - Tuned Hyperparameter: Number of neighbors (K). - Impact: Choosing an appropriate K balances bias and variance. - Real-World Use Case: Recommending similar products to users. 5. 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 𝐕𝐞𝐜𝐭𝐨𝐫 𝐌𝐚𝐜𝐡𝐢𝐧𝐞𝐬 (𝐒𝐕𝐌) - Tuned Hyperparameter: Kernel type (linear, polynomial, or radial basis function). - Impact: The right kernel affects decision boundary flexibility. - Real-World Use Case: Classifying images (e.g., cat vs. dog). By the way, hyperparameter tuning is an iterative process, and understanding the trade-offs helps create robust machine learning models. - - - - - If you've read so far, do LIKE the post 👍🏻 👉🏻 Follow me Piku Maity for more insights on Data & AI ♻ Please do share and repost it to help others Happy Learning!!✌🏻 #datascience #machinelearning #deeplearning #ai #hyperparametertuning #aimodels #mlmodels #modeltraining #predictivemodelling

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Statistics isn't just about numbers—it's about making sense of data and drawing meaningful conclusions. These are some key concepts that shape our understanding: ➡️ Correlation: Not everything that moves together is connected! Correlation shows a relationship between variables, but correlation ≠ causation. Always dig deeper before jumping to conclusions. ➡️ P-Value: This is your tool for significance. A small p-value (usually <0.05) means the results are unlikely to be due to chance. But remember—it's not the whole story. ➡️ Survivorship Bias: Be careful not to only focus on winners. Ignoring failures (or non-survivors) can lead to faulty conclusions about success. ➡️ Simpson’s Paradox: Grouped data can show trends that disappear or reverse when you break the data down. Always check the underlying factors. ➡️ Central Limit Theorem: The magic of large samples! No matter the distribution, the sample mean will approach a normal distribution as sample size increases. This is why big data is so powerful! ➡️ Bayes Theorem: The foundation of probabilistic thinking. It helps update the probability of an event based on new evidence, helping us make informed decisions under uncertainty. ➡️ Law of Large Numbers: The more trials you run, the closer the observed outcomes get to the expected value. Consistency comes with scale. ➡️ Selection Bias: Be cautious! If your sample isn't representative, your findings will be skewed. Ensure you're looking at the right data. ➡️ Outliers: These can skew results, but they might also tell an important story. Always explore them before deciding to remove or include them in your analysis. 10 Best Statistics Courses on Coursera-> https://lnkd.in/gfmzD3jM Image Credit-> Data Interview #Statistics #DataScience #MachineLearning #AI #DataAnalysis #StatisticalThinking #BigData #Analytics

The machine learning (ML) lifecycle can seem complex and intimidating, but it's essentially a journey from asking a business question to getting a model into production and delivering real-world value. Today, we'll break down this journey into 5 key stages:🚀 1. Business Understanding & Data Acquisition: Business Question: It all starts with a clear business question that ML can help answer. What problem are you trying to solve, or what opportunity are you trying to seize? Data Acquisition: Once you have a question, you need the data to answer it. This involves identifying, collecting, and cleaning relevant data sources. 2. Data Preparation & Feature Engineering: Data Preparation: Raw data is rarely ready for ML models. This stage involves cleaning, transforming, and formatting the data to make it usable. Feature Engineering: Extracting meaningful features from the data is crucial for model performance. This may involve creating new features, combining existing ones, or using feature selection techniques. 3. Model Training & Experimentation: Model Training: Here's where the magic happens! You choose an ML algorithm and train it on your prepared data. This involves iterating on different models and hyperparameters to find the best fit. Model Evaluation: Don't fall in love with your first model! Rigorously evaluate its performance using relevant metrics and compare it to other models or baselines. 4. Deployment & Monitoring: Deployment: Once you have a winning model, it's time to put it to work! This involves deploying the model to a production environment where it can make real-time predictions. Monitoring: Even in production, models need monitoring. Track the model's performance, identify and address any issues, and ensure it's delivering value. 5. Feedback Loop & Continuous Improvement: Feedback Loop: The ML lifecycle is not linear. Continuously gather feedback from the business and stakeholders, use it to improve the model's performance, and re-evaluate the business question. Continuous Improvement: Machine learning is an iterative process. As new data becomes available and business needs evolve, be ready to adapt and improve your models. 🔹Remember, the ML lifecycle is a collaborative effort. Data scientists, engineers, business stakeholders, and domain experts all play crucial roles in bringing successful ML projects to life. By following these stages and fostering a collaborative culture, you can unlock the true potential of ML and turn your business questions into real-world impact. Have I overlooked anything? #ml #machinelearning #ai #artificialintelligence #datascience #datascientist #dataanalyst