Predictive analysis uses known data to train models that predict future values. From traditional methods like regression to advanced neural networks, these algorithms revolutionize how we interpret data and make decisions. Read our blog to see how: https://lnkd.in/gyT-DSev
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🔍 Overfitting: The Fine Line Between Accuracy and Generalization 🔍 Overfitting occurs when a machine learning model learns the details and noise in the training data to the extent that it negatively impacts the model’s performance on new data. In essence, the model becomes too "fit" to the training data, capturing patterns that do not generalize beyond the specific dataset. 🛠 How to Identify Overfitting: 1. High Accuracy on Training Data, Low Accuracy on Test Data: - A clear sign of overfitting is when your model achieves very high accuracy on the training data but performs poorly on the test data. 2. Complex Decision Boundaries: - Overfitting often results in overly complex decision boundaries. For example, a linear model might have a simple, straight decision boundary, while an overfitted model might have a highly convoluted boundary that perfectly fits the training data but fails to generalize. 3. Performance Metrics: - Key metrics like precision, recall, and F1-score might show high variance between training and validation sets. 🔧 Causes of Overfitting: 1. Too Complex Models: - Highly complex models, such as deep neural networks with many layers or decision trees with a large number of branches, have the capacity to model noise in the data, leading to overfitting. 2. Insufficient Training Data: - With too little data, the model may learn the noise in the data rather than the true underlying patterns. 3. Too Many Features: - If the model includes too many features, especially irrelevant ones, it may overfit by trying to use every feature to make decisions, even if some of them are just noise. 🛡 How to Prevent Overfitting: 1. Cross-Validation: - Use techniques like k-fold cross-validation to ensure that your model is validated on multiple subsets of the data. 2. Simplify the Model: - Use simpler models with fewer parameters. For example, regularization techniques like L1 (Lasso) and L2 (Ridge) penalize complex models by adding a penalty for larger coefficients, effectively reducing overfitting. 3. Prune Decision Trees: - In tree-based models, you can prune the tree by setting a maximum depth or minimum number of samples per leaf. 4. Early Stopping: - In iterative algorithms like gradient descent, stop training when performance on the validation set starts to degrade. 5. Increase Training Data: - More data generally helps the model generalize better. Data augmentation techniques can also be useful in artificially increasing the size of the training set. 6. Feature Selection: - Reduce the number of features by selecting only the most relevant ones, which reduces the model’s capacity to overfit. 7. Ensemble Methods: - Use ensemble methods like Random Forests, Bagging, or Boosting, which combine the predictions of multiple models to reduce the likelihood of overfitting. For more such insights, follow Tejas S and join the conversation. #MachineLearning #AI #DataScience #DeepLearning
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Bias and Variance in Machine Learning: Striking the Right Balance In the vast landscape of machine learning, two critical concepts play a pivotal role in shaping the performance of our models: bias and variance. These two adversaries often engage in a delicate dance, influencing how well our models generalize to unseen data. Let’s explore their nuances, understand their impact, and discover strategies to strike the right balance. 1. Bias: The Underfitting Culprit Bias represents the model’s inability to capture the underlying complexity of the data. Imagine assuming that the data follows a simple linear function when, in reality, it dances to a more intricate tune. 💡 Here’s what you need to know about bias: Bias occurs due to incorrect assumptions during model training. √Effect: High bias leads to underfitting, where the model oversimplifies the problem and fails to capture essential patterns. √Complexity Boost: Consider using a more complex model (e.g., deep neural networks with additional hidden layers) to better fit the data. √Feature Expansion: Add more features to enhance the model’s ability to capture underlying trends. √Regularization: Adjust regularization strength (e.g., L1 or L2 regularization) to prevent overfitting. 2. Variance: The Overfitting Nemesis Variance, on the other hand, emerges from the model’s sensitivity to variations in the training data. It craves complexity, but too much of it can lead to overfitting. 📑 Here’s the scoop on variance: Variance arises when the model is too sensitive to training data fluctuations. 💡Effect: High variance results in overfitting, where the model fits the training data perfectly but struggles with unseen data. 📊Simplicity Check: Opt for simpler models to reduce variance. 📈Regularization: Strengthen regularization to tame the model’s wild fluctuations. 📑More Data: Gather more training data to stabilize the model’s behavior. 📃The Bias-Variance Tradeoff Ah, the delicate balance! The bias-variance tradeoff dictates that as we reduce bias, variance tends to rise, and vice versa. Our goal? Find the sweet spot where both errors are minimized. 🎯 Conclusion: In the grand symphony of machine learning, bias and variance dance together, shaping our models’ destiny. Remember, a dash of bias and a sprinkle of variance can lead to a harmonious melody of predictive power. 🎶 #machinelearning #biasandvariance #datascience #modeling #underfitting #overfitting #lassoridgeregression
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Most of us are acquainted with contemporary terms like Artificial Intelligence, Machine Learning, Deep Learning, Smart City, and various other modern technologies. But few of us know that the foundation of all these technologies lies in Data. Therefore, having knowledge about Data Science holds paramount importance. I have recently published an article on this topic in the newspaper. I encourage you to read it and share your feedback. #datascience #dataandanalytics #machinelearning #banking #bankingtechnology #artificialintelligence #deeplearning #moderntechnology #industry4point0 #businessintelligence #mis #ai
Powering the Future : Data Science Fuels the Modern Technologies
dailymessenger.net
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𝐇𝐄𝐀𝐃 𝐎𝐅 𝐌𝐀𝐑𝐊𝐄𝐓𝐈𝐍𝐆 & 𝐁𝐑𝐀𝐍𝐃 | Team Leadership | Product Marketing Management | Technology & AI | Brand Positioning | Tech Innovator | Proud Dad w/ Toddler Son 🍼 & Clumsy Surfer
📉 The Hidden Pitfalls of Recursive Data Training in AI Models 🌐 Just read an insightful article in Nature titled "AI models collapse when trained on recursively generated data" that delves into a crucial issue in AI development. The study reveals that AI models, when trained on data generated by other AIs, can suffer from performance degradation and even collapse over time. This recursive training problem highlights the importance of diverse and high-quality training data to maintain model robustness and accuracy. In the fast-evolving field of AI, understanding and mitigating such pitfalls is essential for sustainable progress. This research underscores the need for continuous innovation and vigilance in AI model training practices. #AI #MachineLearning #DataScience #ArtificialIntelligence #TechInnovation #ResearchInsights https://lnkd.in/gN_NKy4k
AI models collapse when trained on recursively generated data - Nature
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Tableau Developer @ Infosys || Tableau, SQL, Alteryx, Data Analysis || Utilizing data to generate compelling and visually engaging dashboards
DATA ANALYSIS NOTES (Part 1): 1. 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 is the process of identifying, cleaning, transforming and modelling data to discover meaningful and useful information. 2. To analyze data, core components of analytics are divided into: a. 𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲: help to answer questions about what has happened based on historical data. Example: Is generating reports to provide a view of an organization’s sales and financial data. b. 𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝘁𝗶𝗰: help to answer questions about why events happened. The process occurs in three steps: i. Identify anomalies in the data. These anomalies might be unexpected changes in a metric or a particular market. ii. Collect data that is related to these anomalies. iii. Use statistical techniques to discover relationships and trends that explain these anomalies. c. 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲: help to answer questions about what will happen in the future. Techniques include various statistical and ML techniques such as neural networks, decision trees and regression. d. 𝗣𝗿𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲: help to answer questions about which actions should be takes to achieve a goal or target. This technique relies on ML as one of the strategies to find patterns in large semantic models. By analyzing past decisions and events, organizations can estimate the likelihood of different outcomes. e. 𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲: Attempts to draw inferences from existing data and patterns, derive conclusions based on existing knowledge bases, and the add these findings back into the knowledge base for future inferences, a self-learning feedback loop. Help you learn what might happen if circumstances change and determine how you might handle these situations. #dataanalytics #data #microsoftlearn
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This reads like a total game changer for data analysis. Explainable probabilistic models, calibrated uncertainty, more accurate and many times faster than baseline methods using neural networks. Imagine the impact this technology could have in this polarized world of manipulated statistics, bad data, and eroded confidence in experts. #ai #genai #sql https://lnkd.in/eDQfJD59
MIT researchers introduce generative AI for databases
news.mit.edu
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By David Lance, November 30, 2023. At its core, Artificial Intelligence (AI) is the product of two components –the first is data and the second is algorithms. Diverse types of algorithms exist, each potentially displaying a level of complexity. Among these, neural networks stand out as multi-layered constructs that emulate the human approach to problem-solving. Neural networks are multi-layer algorithms designed to mimic the human way of approaching problem-solving. #modelvalidation #modeltraining #modelevaluation #datacollection #datapreprocessing #datautilization #aidata
Data and Algorithms: The Building Blocks of Artificial Intelligence
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|Business Analyst | Data Analysis | Data Engineering | Licensed Realtor | Collating | Python | R | SAS | SQL | Cloud | VBA | Tableau | Power BI | reporting analyst| MS Office |
After binning the continuous variables to handle outliers, you can use the binned features to build a predictive model. The type of model you choose depends on your specific problem, data characteristics, and performance requirements. Here are steps to build a predictive model after binning: 1. **Select Model**: Choose a suitable machine learning model based on your problem. Common choices include linear regression, logistic regression, decision trees, random forests, gradient boosting machines (GBMs), support vector machines (SVMs), and neural networks. 2. **Feature Engineering**: After binning, you may want to perform additional feature engineering, such as creating interaction terms, polynomial features, or domain-specific transformations. This can help improve the model's predictive performance. 3. **Split Data**: Split your dataset into training and testing sets. The training set will be used to train the model, while the testing set will be used to evaluate its performance. 4. **Model Training**: Train the selected model on the training data using the binned features as input and the target variable as the output. Use appropriate techniques for model validation, such as cross-validation, to assess the model's generalization performance. 5. **Model Evaluation**: Evaluate the trained model's performance on the testing data using appropriate evaluation metrics. For regression tasks, common metrics include mean squared error (MSE), mean absolute error (MAE), and R-squared. For classification tasks, metrics like accuracy, precision, recall, F1-score, and ROC-AUC can be used. 6. **Hyperparameter Tuning**: Fine-tune the model's hyperparameters to optimize its performance. This can be done using techniques like grid search, random search, or Bayesian optimization. 7. **Model Interpretation**: Depending on the model type, you may want to interpret the model's predictions to gain insights into the underlying relationships between the features and the target variable. Techniques like feature importance analysis, partial dependence plots, and SHAP (SHapley Additive exPlanations) values can help interpret the model's behavior. 8. **Deployment**: Once you are satisfied with the model's performance, deploy it into production for making predictions on new, unseen data. Ensure that the deployment process is robust and scalable. 9. **Monitoring and Maintenance**: Continuously monitor the model's performance in production and update it as needed to maintain its effectiveness over time. This may involve retraining the model with new data or updating its parameters based on changing business requirements. Remember that the choice of model and the specific implementation details will vary based on your problem domain, data characteristics, and available resources. Experimentation and iteration are key to developing a successful predictive model after binning the features to handle outliers.
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Data Analytics & Quantitative Finance Professional | M.A in Financial Economics (University of Madras,Chennai) | M.sc Computational Statistics & Applied A.I (Christ University, Bangalore)
📍Overfitting and underfitting are two common challenges encountered when training regression models, which can significantly impact their performance and reliability. Overfitting occurs when a model learns to capture noise or random fluctuations in the training data rather than the underlying patterns. This results in a model that performs well on the training data but fails to generalize to unseen data. Essentially, the model memorizes the training data instead of learning the underlying relationships, leading to poor performance on new observations. On the other hand, underfitting happens when a model is too simple to capture the underlying structure of the data. In this case, the model fails to capture the patterns in the training data and performs poorly both on the training and unseen data. Underfitting often occurs when the model is too simplistic or when important features are not included in the model, leading to biased predictions. To address these issues and build robust regression models, it's essential to understand the causes and implications of overfitting and underfitting. Here are some strategies to mitigate these problems: 🪶 **Cross-validation**: Splitting the dataset into multiple subsets for training and evaluation can help assess the model's performance on unseen data. Techniques like k-fold cross-validation can provide a more accurate estimate of the model's generalization error. 🪶 **Feature selection**: Identifying and selecting the most relevant features can help prevent overfitting by reducing the complexity of the model. Feature engineering techniques such as regularization and dimensionality reduction can aid in selecting informative features while discarding noise. 🪶 **Regularization**: Introducing regularization techniques such as L1 (Lasso) and L2 (Ridge) regularization can prevent overfitting by penalizing overly complex models. These techniques add a penalty term to the loss function, encouraging the model to prioritize simpler solutions. 🪶 **Model complexity control**: Tuning hyperparameters such as the model's complexity (e.g., tree depth in decision trees, number of hidden layers in neural networks) can help strike a balance between bias and variance. Regularly validating the model's performance on a separate validation set can guide the selection of optimal hyperparameters. 🪶 **Ensemble methods**: Combining multiple models (e.g., random forests, gradient boosting) can help mitigate the risk of overfitting and underfitting by leveraging the wisdom of crowds. Ensemble methods aggregate the predictions of multiple base models, often resulting in improved generalization performance. 🔎By understanding the nuances of overfitting and underfitting and employing appropriate strategies to mitigate these challenges, machine learning practitioners can develop regression models that generalize well to unseen data, enabling more reliable predictions and insights in various domains.
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Great dad | Inspired Risk Management and Security Profesional | Cybersecurity | Leveraging Data Science & Analytics My posts and comments are my personal views and perspectives but not those of my employer
👀 As AI and especially LLMs keep evolving in speed, sophistication, and complexity, there is a new risk and challenge to overcome, and the key is that there is not too much time to solve it or at least mitigate it. In the near future the LLM training will be increasingly trained or fine-tuned with text, images and other synthetic data generated by other AI systems. 🤯 This could potentially pollute data or results with bias or wrong facts or fake facts. 🤔 What is model collapse? Model collapse is a degenerative process affecting generations of learned generative models, in which the data they generate end up polluting the training set of the next generation. Being trained on polluted data, they then misperceive reality. #ai #genai #aitrends #bias #aimodels #reality
AI models collapse when trained on recursively generated data - Nature
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