Difference between revisions of "...predict categories"
m |
|||
| (25 intermediate revisions by the same user not shown) | |||
| Line 1: | Line 1: | ||
| − | + | {{#seo: | |
| − | + | |title=PRIMO.ai | |
| + | |titlemode=append | ||
| + | |keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | ||
| − | + | <!-- Google tag (gtag.js) --> | |
| + | <script async src="https://www.googletagmanager.com/gtag/js?id=G-4GCWLBVJ7T"></script> | ||
| + | <script> | ||
| + | window.dataLayer = window.dataLayer || []; | ||
| + | function gtag(){dataLayer.push(arguments);} | ||
| + | gtag('js', new Date()); | ||
| + | |||
| + | gtag('config', 'G-4GCWLBVJ7T'); | ||
| + | </script> | ||
| + | }} | ||
| + | * [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Train, Validate, and Test]] | ||
| + | |||
| + | Do you have... | ||
| + | * ...two-class classification; two predicting categories? | ||
| + | ** Do you need the results to be explainable? | ||
| + | *** Yes | ||
| + | **** fast training, accurate, and can have a large footprint, then try the [[(Boosted) Decision Tree]] | ||
| + | **** linear, greater than 100 features, then try the [[Support Vector Machine (SVM)]] | ||
| + | *** No | ||
| + | **** fast training, linear, and the features are independent, then try the two-class [[Bayes#Naive Bayes|Naive Bayes]] point machine | ||
| + | * ...multi-class classification; three or more categories? | ||
| + | ** Do you need the results to be explainable? | ||
| + | *** Yes | ||
| + | **** fast training, linear, then try the [[Logistic Regression (LR)]] | ||
| + | **** accurate, then try the [[Decision Jungle]] for multi-class classification | ||
| + | **** fast training, accurate, then try the [[Random Forest (or) Random Decision Forest]] | ||
| + | *** No | ||
| + | **** linear, then try the [[Bayes#Naive Bayes|Naive Bayes]] | ||
| + | **** accurate, can allow long training times, then try the [[Neural Network#Deep Neural Network (DNN)|Deep Neural Network (DNN)]] e.g. [[Image Classification]] | ||
| + | **** which is a type of is predecessors... [[Feed Forward Neural Network (FF or FFNN)]] and [[Neural Network]] | ||
___________________________________________________ | ___________________________________________________ | ||
| − | |||
| − | * | + | * [[Data Quality#Data Augmentation, Data Labeling, and Auto-Tagging|Data Augmentation, Data Labeling, and Auto-Tagging]] |
| − | + | * [https://medium.com/@srnghn/machine-learning-trying-to-predict-a-categorical-outcome-6ba542b854f5 Machine Learning: Trying to classify your data | Stacey Ronaghan - Medium] | |
| − | + | * [[Evaluating Machine Learning Models]] | |
| − | * | + | ** [https://bookdown.org/max/FES/encoding-categorical-predictors.html Feature Engineering and Selection: A Practical Approach for Predictive Models - 5 Encoding Categorical Predictors | Max Kuhn and Kjell Johnson] |
| − | ** | + | ** [https://docs.aws.amazon.com/machine-learning/latest/dg/binary-model-insights.html Binary Model Insights |] [[Amazon | Amazon Web Services]] |
| − | * ... | + | ** [https://docs.aws.amazon.com/machine-learning/latest/dg/multiclass-model-insights.html Multiclass Model Insights |] [[Amazon | Amazon Web Services]] |
| − | + | ||
| − | ** | + | |
| − | + | Classification problems are sometimes divided into binary (yes or no) and multi-category problems (animal, vegetable, or mineral). Classifiers are ubiquitous in data science. The world around is full of classifiers. Classifiers help in identifying customers who may churn. Classifiers help in predicting whether it will rain or not. Classifiers help in preventing spam e-mails. If the targets are designed to be binary (two-class classification) then a binary classifier is used, the target will only take a 0 or 1 value. [https://www.infoworld.com/article/3394399/machine-learning-algorithms-explained.html Machine learning algorithms explained | Martin Heller - InfoWorld] | |
| − | |||
| − | |||
| − | |||
Latest revision as of 22:52, 5 March 2024
- AI Solver ... Algorithms ... Administration ... Model Search ... Discriminative vs. Generative ... Train, Validate, and Test
Do you have...
- ...two-class classification; two predicting categories?
- Do you need the results to be explainable?
- Yes
- fast training, accurate, and can have a large footprint, then try the (Boosted) Decision Tree
- linear, greater than 100 features, then try the Support Vector Machine (SVM)
- No
- fast training, linear, and the features are independent, then try the two-class Naive Bayes point machine
- Yes
- Do you need the results to be explainable?
- ...multi-class classification; three or more categories?
- Do you need the results to be explainable?
- Yes
- fast training, linear, then try the Logistic Regression (LR)
- accurate, then try the Decision Jungle for multi-class classification
- fast training, accurate, then try the Random Forest (or) Random Decision Forest
- No
- linear, then try the Naive Bayes
- accurate, can allow long training times, then try the Deep Neural Network (DNN) e.g. Image Classification
- which is a type of is predecessors... Feed Forward Neural Network (FF or FFNN) and Neural Network
- Yes
- Do you need the results to be explainable?
___________________________________________________
- Data Augmentation, Data Labeling, and Auto-Tagging
- Machine Learning: Trying to classify your data | Stacey Ronaghan - Medium
- Evaluating Machine Learning Models
Classification problems are sometimes divided into binary (yes or no) and multi-category problems (animal, vegetable, or mineral). Classifiers are ubiquitous in data science. The world around is full of classifiers. Classifiers help in identifying customers who may churn. Classifiers help in predicting whether it will rain or not. Classifiers help in preventing spam e-mails. If the targets are designed to be binary (two-class classification) then a binary classifier is used, the target will only take a 0 or 1 value. Machine learning algorithms explained | Martin Heller - InfoWorld
