1.0KaLabshttps://karthicklakshmanan.comkarthickhttps://karthicklakshmanan.com/index.php/author/karthick/rich600338<blockquote class="wp-embedded-content" data-secret="y3okVGWhpc"><a href="https://karthicklakshmanan.com/index.php/knowledge-base/ai-model-compendium/">AI Model Compendium</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://karthicklakshmanan.com/index.php/knowledge-base/ai-model-compendium/embed/#?secret=y3okVGWhpc" width="600" height="338" title="“AI Model Compendium” — KaLabs" data-secret="y3okVGWhpc" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script> /*! This file is auto-generated */ !function(d,l){"use strict";l.querySelector&&d.addEventListener&&"undefined"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!/[^a-zA-Z0-9]/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret="'+t.secret+'"]'),o=l.querySelectorAll('blockquote[data-secret="'+t.secret+'"]'),c=new RegExp("^https?:$","i"),i=0;i<o.length;i++)o[i].style.display="none";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute("style"),"height"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):"link"===t.message&&(r=new URL(s.getAttribute("src")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener("message",d.wp.receiveEmbedMessage,!1),l.addEventListener("DOMContentLoaded",function(){for(var e,t,s=l.querySelectorAll("iframe.wp-embedded-content"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute("data-secret"))||(t=Math.random().toString(36).substring(2,12),e.src+="#?secret="+t,e.setAttribute("data-secret",t)),e.contentWindow.postMessage({message:"ready",secret:t},"*")},!1)))}(window,document); </script> COMPLETE AI MODEL REFERENCE — Full COMPLETE AI MODEL REFERENCE Concise descriptions, difficulty level, typical uses and example projects for major AI, ML and deep learning models (comprehensive 2025‑level list). Jump to: Classical ML Neural Networks Transformers & LLMs Graph & Relational Reinforcement Learning Generative & Diffusion Hybrid & Agentic Tools Classical Machine Learning Models Model Level Description Common Uses / Example Projects Linear Regression Beginner Predict continuous targets via linear combination of features; teaches OLS and gradients. House price prediction; sales/time-series forecasting; energy consumption modeling; baseline regression experiments; feature selection studies. Logistic Regression Beginner Binary classification using sigmoid; outputs probabilities and interpretable coefficients. Spam detection; medical screening; churn prediction; credit default classification; simple NLP classification with bag‑of‑words. Decision Tree Beginner Hierarchical splits on features producing human‑readable rules; easy to visualize. Credit scoring rules; diagnostic flowcharts; interpretable classification demos; feature importance visualizer; teaching decision logic. Random Forest Intermediate Ensemble of randomized trees; reduces variance and overfitting via averaging. Tabular baseline for industry problems; feature importance reports; anomaly detection; ecology / bioinformatics classification; model stacking component. Gradient Boosting (XGBoost / LightGBM / CatBoost) Intermediate Sequentially built trees that focus on correcting prior errors; state‑of‑the‑art for tabular tasks. Kaggle‑style tabular pipelines; credit […]