Podcast
Questions and Answers
Machine learning techniques can struggle with predictions when there is no historical ______ available.
Machine learning techniques can struggle with predictions when there is no historical ______ available.
data
According to Wong, Sen, and Chiang (2012), Twitter's ability to predict Oscar winners is ______.
According to Wong, Sen, and Chiang (2012), Twitter's ability to predict Oscar winners is ______.
limited
Data mining techniques may not reflect ______ that appear on other websites.
Data mining techniques may not reflect ______ that appear on other websites.
reviews
One limitation of prediction models is their ______ on historical data.
One limitation of prediction models is their ______ on historical data.
Predictions based on big data can be called into ______ due to their limitations.
Predictions based on big data can be called into ______ due to their limitations.
Prediction is an estimation or forecast of future outcomes based on knowledge of the ______.
Prediction is an estimation or forecast of future outcomes based on knowledge of the ______.
The process of determining the parameters of a model using sample data is referred to as ______.
The process of determining the parameters of a model using sample data is referred to as ______.
The goal of developing a predictive model should be minimizing ______ rather than eliminating them.
The goal of developing a predictive model should be minimizing ______ rather than eliminating them.
Each class of entities is modeled using a set of parameters and their corresponding ______.
Each class of entities is modeled using a set of parameters and their corresponding ______.
Assuming that the underlying patterns are ______, learning from the past allows us to predict the future.
Assuming that the underlying patterns are ______, learning from the past allows us to predict the future.
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Study Notes
Prediction Techniques
- Prediction involves estimating future outcomes based on past knowledge.
- Identifying past influencing factors is crucial for accurate forecasting.
- Assumption that underlying patterns remain stable aids in learning from historical data.
- Models are created through a systematic prediction process ensuring standard considerations are met.
- Aim of predictive models is minimizing errors, recognizing that complete elimination of errors is impossible.
- Model development must address correlations between characteristics in the data.
- Training involves learning from sample data to form a parametric model that classifies unknown entities based on selected features and their weights.
Neural Networks and Deep Learning
- Neural networks are powerful tools for learning complex patterns and solving intricate problems.
- Deep networks, with multiple hidden layers, offer greater learning capacity than shallow networks.
- Increased number of free parameters (weights) in deep networks contributes to enhanced power.
- Training deep networks is slower and may require advanced techniques to expedite learning.
- Success stories of deep learning include applications in image recognition, speech recognition, and natural language processing (NLP).
Limitations of Prediction Models
- Use of machine learning and data mining in predictions presents challenges and limitations.
- Study on 1.7 million tweets showed that social media data may not reliably predict outcomes, such as Oscar winners or box office revenue.
- Predictions depend on the availability of historical data; lack of data can result in poor model performance.
- Selection of comprehensive input characteristics is essential, as missing factors may render models inaccurate.
- Characteristics of the entity being modeled may evolve over time, necessitating ongoing performance monitoring and model adjustments.
- Uncertainty is inherent in real-world predictive models, yet identified patterns are generally considered stable enough for future predictions.
Regression Analysis
- Regression analysis focuses on predicting and forecasting by examining relationships between variables.
- It determines the connection between a dependent variable (target) and one or more independent variables (predictors).
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