Papers With Backtest’s Post

Balancing Model Fit and Generalization: The Bias Variance Tradeoff 💥💥 GET FULL SOURCE CODE AT THIS LINK 👇👇 👉 https://lnkd.in/dRwTWEVU Model fitting is a crucial step in machine learning, but it's equally important to ensure that the model generalizes well to unseen data. The bias-variance tradeoff is a fundamental concept in machine learning that helps achieve this balance. Bias refers to the error introduced by simplifying a model, while variance refers to the error introduced by a model's complexity. When a model is too simple, it may not capture the underlying patterns in the data, leading to high bias. On the other hand, a complex model may overfit the training data, resulting in high variance. The ideal model should balance these two extremes. The bias-variance tradeoff has significant implications for model selection, regularization techniques, and hyperparameter tuning. Understanding this tradeoff is essential for anyone working with machine learning models. To reinforce your understanding of the bias-variance tradeoff, try experimenting with different models on a familiar dataset. Observe how the model's complexity affects its performance on the training and testing sets. You can also explore techniques like cross-validation and regularization to see how they impact the bias-variance tradeoff. Additional Resources: For a deeper dive into the bias-variance tradeoff, you can refer to the following resources: * Chapter 7 of "Pattern Recognition and Machine Learning" by Christopher Bishop * Section 5 of "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman #BiasVarianceTradeoff #MachineLearning #ModelSelection #Regularization #HyperparameterTuning #CrossValidation #stem #MachineLearningTheory #DataScience #ArtificialIntelligence #ModelFitting #GeneralizationError Find this and all other slideshows for free on our website: https://lnkd.in/dRwTWEVU #BiasVarianceTradeoff #MachineLearning #ModelSelection #Regularization #HyperparameterTuning #CrossValidation #stem #MachineLearningTheory #DataScience #ArtificialIntelligence #ModelFitting #GeneralizationError https://lnkd.in/dcNG55XR

🚀 New Blog Alert! 🚀 Just published the first part Classification Models! 🎯 In this blog, I dive into Logistic Regression and explain why linear regression isn't suitable for classification problems. You'll learn about: 1. Why logistic regression works better for classification 🚫 2. Maximum Likelihood Estimation for coefficient prediction 🔢 3. Training logistic regression models, both simple and with multiple predictors 💻 4. Adjusting the probability threshold and working with confusion matrices 📊 5. Key performance measures for classification models 🔍 Dataset and code available on: https://lnkd.in/gSfSsY_2 Check it out and share your thoughts! #MachineLearning #DataScience #LogisticRegression #ClassificationModels #TechBlog

I’m happy to share this quantitative financial machine learning [ML] predictive model. #quantfinance #quantitativefinance

Exploring non-linear relationships in regression? Transformations can lead to trillions of potential models, but is there a better way? Samuele Mazzanti's article discusses how gradient-boosting models can provide effective solutions with ease. #Regression #DataScience

Exploiting stock market via the Bayesian nature of Gaussian Process Regression, via mean-variance optimisation with respect to the predictive uncertainty distribution with machine learning models statistically out-of-sample R-squared & Sharpe ratio of prediction-sorted portfolios. ;)))

Decision Tree Regression is a machine learning algorithm that predicts continuous target values by recursively splitting data into subsets based on feature values, creating a tree structure. Each split minimizes the Mean Squared Error (MSE), aiming to group similar target values together in leaf nodes, where the final prediction is typically the average of values in that node. Known for its interpretability and ability to capture non-linear relationships, Decision Tree Regressors are prone to overfitting but can be controlled with parameters like maximum depth and minimum samples per leaf. #decisiontreeregression #decisiontree #mlalgorithm #datascience

To all that say #ML is #statistics. It is not and I am tiered of reading it. #statistics is a field of #probability while #fuzzylogic on the other hand is #deterministic That means a probability of 80% means we get 4 answers A and 1 answer B (like a coin toss or quantum probability) Fuzzy logic on the other hand IS DETERMINISTC which means a #threshhold of 80% will always (100%) yield answer A and never answer B. If you understand this, congratulations you are probably 1 in 1 million.

Hey everyone! 👋 I just published a new blog on Logistic Regression – one of the key tools for solving binary classification problems. If you're curious about how it works or need a clear explanation with real-life examples, I’d love for you to check it out! 🚀 Whether you’re into data science, machine learning, or just brushing up on your skills, I’ve broken it down in a way that’s easy to follow. Plus, I’ve included some great resources to dive deeper! Would love to hear your thoughts or any feedback. 😊 #DataScience #LogisticRegression #MachineLearning #BinaryClassification

Step 1: You understand Linear regression, you will realize curve fitting Step 2: With logistic regression, you will know about nonlinear activation functions Step 3: Then you will understand cross-entropy loss, the holy grail of machine learning losses Step 4: As you progress toward maximum likelihood estimation you will get the essence of probability and optimization Then the actual fun of function approximation begins. Most people never reach step 4.

Amazing steps - log likelihood is the key to understanding ML. Learn log likelihood today - https://lnkd.in/g4ETc6P9