Classification
Artificial Intelligence
Extending PAC Learning to a Strategic Classification Setting
Why Strategic Classification Is Useful: MotivationBinary classification is a cornerstone of machine learning. It was the primary topic I used to be taught once I took an introductory course on the topic; the real-world...
Artificial Intelligence
Which Features Are Harmful For Your Classification Model?
Feature importance is probably the most common tool for explaining a machine learning model. It's so popular that many data scientists find yourself believing that feature importance equals feature goodness.It just isn't so.When a...
Artificial Intelligence
Text Classification Challenge with Extra-Small Datasets: Positive-Tuning Versus ChatGPT The dataset Regular fine-tuning with RoBERTa Few-shot with ChatGPT Positive-tuning a GPT-3 model Conclusion Sources
LLMs excel on extra-small datasets, but classical approaches shine as datasets growOnce more, performance was heavily influenced by the prompt and the samples provided. The model also generated several categories outside the goal list,...
Artificial Intelligence
Predict Player Churn, with Some Help From ChatGPT Introduction The Platform The Dataset Exploratory Data Evaluation Training a Classification Model Improving the Model Performance Creating Recent Features Training a Recent (hopefully improved)...
These curves are also useful to find out what threshold we could use in our final application. For instance, whether it is desired to reduce the variety of false positives, then we will select...
Artificial Intelligence
Naive Bayes Classification Background: Bayes’ Theorem The Naive Bayes Model Bernoulli Naive Bayes Categorical Naive Bayes Multinomial Naive Bayes Laplace Smoothing Gaussian Naive Bayes Naive Bayes Classifiers in Scikit-Learn Document Classification Example Summary Final Notes
The event models described above will also be combined in case we have now a heterogenous data set, i.e., an information set that incorporates several types of features (for instance, each categorical and continuous...
Artificial Intelligence
Deep Dive into Softmax Regression Background: Multi-Class Classification Problems The Softmax Regression Model Cross-Entropy Loss Gradient Descent Practice Query Softmax Regression in Scikit-Learn Summary
With these gradients, we will use (stochastic) gradient descent to reduce the loss function on the given training set.You might be given a set of images and you must classify them into dogs/cats and...
Artificial Intelligence
Mastering Logistic Regression Background: Binary Classification Problems The Logistic Regression Model Log Loss Gradient Descent Implementation in Python The LogisticRegression Class in Scikit-Learn Summary
The next plot shows the log loss when y = 1:The log loss equals to 0 only in case of an ideal prediction (p = 1 and y = 1, or p = 0...
Artificial Intelligence
Digit Classification using deep learning model created using R language. Introduction: Importing crucial libraries: Loading and Preparing MNIST Preparing MNIST and the Collected Handwritten Digits for Training Train and...
Above scores look acceptable and further training could improve performance of our model as well.Lastly, We save our model in order that we could train it again and deploy it as well if model...
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