Reporting and Analysis
•Examples
•Results section: Page 51: STOCK MARKET PREDICTION USING ENSEMBLE OF GRAPH
THEORY, MACHINE LEARNING AND DEEP LEARNING MODELS
•https://scholarworks.sjsu.edu/cgi/viewcontent.cgi?article=1692&context=etd_projects
•Check Results and Discussion sections
•https://arxiv.org/ftp/arxiv/papers/2203/2203.06848.pdf
•A Comparative Study on Forecasting of Retail Sales
May be complicated: Learning Context-Aware Classifier for Semantic Segmentation
•https://arxiv.org/pdf/2303.11633.pdf
•Learning Context-Aware Classifier for Semantic Segmentation
•Check results section; also Discussion Section: SPEECH INTELLIGIBILITY CLASSIFIERS FROM 550K DISORDERED SPEECH SAMPLES
•https://arxiv.org/pdf/2303.07533.pdf
•You can notice: results reported under different criteria, use of tables and figures.
•Notice/read the descriptions
Data Analytics, Machine Learning, Data Science
ROC/AUC: Classification: ROC and AUC
https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc
F1 Score in Machine Learning: https://www.geeksforgeeks.org/f1-score-in-machine-learning/
Ref: https://www.geeksforgeeks.org/f1-score-in-machine-learning/
“This formula ensures that both precision and recall must be high for the F1 score to be high. If either one drops significantly, the F1 score will also drop.”
LEAF and CNN:
LEAF: “Leaf: A learnable frontend for audio classification,” ICLR, 2021
ARIMA: Introducing ARIMA models
https://www.ibm.com/think/topics/arima-model
Autoregressive Integrated Moving Average (ARIMA) Prediction Model
https://www.investopedia.com/terms/a/autoregressive-integrated-moving-average-arima.asp
“What Is an Autoregressive Integrated Moving Average (ARIMA)?
An autoregressive integrated moving average, or ARIMA, is a statistical analysis model that uses time series data to either better understand the data set or to predict future trends.
A statistical model is autoregressive if it predicts future values based on past values. For example, an ARIMA model might seek to predict a stock’s future prices based on its past performance or forecast a company’s earnings based on past periods.” : Ref: Investopedia
GCN: Graph Convolutional Networks (GCNs): Architectural Insights and Applications
“GCNs are tailored to work with non-Euclidean data, making them suitable for a wide range of applications including social networks, molecular structures, and recommendation systems.“
Facebook Prophet:
Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.
https://facebook.github.io/prophet
Single community based linear models:
Google AI Overview:
“Single community-based linear models refer to statistical models where a single linear equation is used to predict a response variable based on the characteristics of a single community or group. These models assume a linear relationship between the predictor variables and the outcome within that specific community”
Multiple Community-Based Linear Models
“The term “Multiple Community-Based Linear Models” likely refers to a modeling framework where separate linear models are fitted for different communities (e.g., neighborhoods, schools, cities, regions), rather than combining all data into a single model.” Reference: ChatGPT Also, this may be reference: https://www.stats.ox.ac.uk/~snijders/mlbook.htm
