Which technique to apply if function Y changes with values of X variables other than linear regression
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Stop reposting the same question all over the site. ONCE in QA is enough.
Asking questions is a skill CodeProject Forum Guidelines Google: C# How to debug code Seriously, go read these articles.
Dave KreskowiakTo be fair, Andre did tell him to repost it here. :)
"These people looked deep within my soul and assigned me a number based on the order in which I joined." - Homer
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To be fair, Andre did tell him to repost it here. :)
"These people looked deep within my soul and assigned me a number based on the order in which I joined." - Homer
Thanks, I made the suggestion as it is an AI related question.
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I have data in two columns, X and Y where the function Y changes for increasing values of the X variable. If Y shows variations with X, the simplest way to do this is by fitting the data with a linear function.(In python), but I am looking for some "advanced" strategy (other than linear regression, i.e., using Neural Networks or other machine learning techniques) to elaborate on these data because i also want to find pattern in it. Need guidance .
This is not remotely in my field of expertise, I decided to ask AI about an AI question. Please check and confirm each suggestion as it is commonly known that false values are given by ChatGPT in certain scenarios. Suggestions given by ChatGPT - You can explore more advanced techniques like polynomial regression, decision trees, random forests, support vector machines, and neural networks. Polynomial Regression: This involves fitting a polynomial equation to the data, allowing for curved relationships between X and Y. You can experiment with different polynomial degrees to capture varying degrees of complexity. Decision Trees and Random Forests: Decision trees split the data based on different conditions and can handle both linear and non-linear relationships. Random forests are an ensemble of decision trees that can handle complex relationships more effectively. Support Vector Machines (SVM): SVMs are good for both linear and non-linear patterns. They aim to find a hyperplane that best separates the data while considering a "kernel trick" to map the data into a higher-dimensional space for better separation. Neural Networks: Neural networks are capable of capturing complex patterns and relationships in data. You can design a network with multiple layers and nodes to model intricate non-linear connections between X and Y. Gradient Boosting: This ensemble technique combines multiple weak learners (usually decision trees) to create a strong predictive model. It's powerful for capturing complex relationships. K-Nearest Neighbors (KNN): This instance-based learning algorithm uses nearby data points to predict the value of a new point. It can capture local patterns that may not be apparent with global models. Clustering Algorithms: If you're interested in discovering inherent patterns in your data without a specific Y variable, clustering techniques like k-means or hierarchical clustering might be useful. Feature Engineering: Transform your features (X variables) to create new ones that might capture complex relationships more effectively. This might involve interactions, logarithmic transformations, or other functions. Time Series Analysis: If your data has a temporal component, time series techniques like ARIMA (AutoRegressive Integrated Moving Average) or LSTM (Long Short-Term Memory) networks could be useful. Dimensionality Reduction: Techniques like Principal Component Analysis (PCA) or t-SNE (t-Distributed Stochastic Neighbor Embedding) can help you visualize and analyze high-dimen
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Thanks, I made the suggestion as it is an AI related question.
I wasn't aware that Q&A was "non-AI" related. It's a poor "AI question" to start with, and the whole concept of "moving the question" has made this painful.
"Before entering on an understanding, I have meditated for a long time, and have foreseen what might happen. It is not genius which reveals to me suddenly, secretly, what I have to say or to do in a circumstance unexpected by other people; it is reflection, it is meditation." - Napoleon I
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I wasn't aware that Q&A was "non-AI" related. It's a poor "AI question" to start with, and the whole concept of "moving the question" has made this painful.
"Before entering on an understanding, I have meditated for a long time, and have foreseen what might happen. It is not genius which reveals to me suddenly, secretly, what I have to say or to do in a circumstance unexpected by other people; it is reflection, it is meditation." - Napoleon I
Hi Gerry, I followed suit as the more experienced posters tend to send al AI questions to the AI forum because, like I do, we tend to think that a better answer or response will be achieved there than in the Q&A. I will refrain from making the suggestion again.
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Hi Gerry, I followed suit as the more experienced posters tend to send al AI questions to the AI forum because, like I do, we tend to think that a better answer or response will be achieved there than in the Q&A. I will refrain from making the suggestion again.
The redirects are usually to people who post questions relating to the CodeProject AI, and we point them to the [CodeProject.AI Discussions](https: //www.codeproject.com/Feature/CodeProjectAI-Discussions.aspx)[^] forum, rather than the general AI one here. And yes, I agree it can be confusing.
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I have data in two columns, X and Y where the function Y changes for increasing values of the X variable. If Y shows variations with X, the simplest way to do this is by fitting the data with a linear function.(In python), but I am looking for some "advanced" strategy (other than linear regression, i.e., using Neural Networks or other machine learning techniques) to elaborate on these data because i also want to find pattern in it. Need guidance .
If the relationship between Y and X variables in a function changes and it's not suited for linear regression like Pin, for an eighth-grade understanding, you might want to explore other methods like graphing, data clustering, or curve fitting to better understand the pattern. These techniques can help you see how Y responds to different X values in a way that goes beyond a straight line.
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This is not remotely in my field of expertise, I decided to ask AI about an AI question. Please check and confirm each suggestion as it is commonly known that false values are given by ChatGPT in certain scenarios. Suggestions given by ChatGPT - You can explore more advanced techniques like polynomial regression, decision trees, random forests, support vector machines, and neural networks. Polynomial Regression: This involves fitting a polynomial equation to the data, allowing for curved relationships between X and Y. You can experiment with different polynomial degrees to capture varying degrees of complexity. Decision Trees and Random Forests: Decision trees split the data based on different conditions and can handle both linear and non-linear relationships. Random forests are an ensemble of decision trees that can handle complex relationships more effectively. Support Vector Machines (SVM): SVMs are good for both linear and non-linear patterns. They aim to find a hyperplane that best separates the data while considering a "kernel trick" to map the data into a higher-dimensional space for better separation. Neural Networks: Neural networks are capable of capturing complex patterns and relationships in data. You can design a network with multiple layers and nodes to model intricate non-linear connections between X and Y. Gradient Boosting: This ensemble technique combines multiple weak learners (usually decision trees) to create a strong predictive model. It's powerful for capturing complex relationships. K-Nearest Neighbors (KNN): This instance-based learning algorithm uses nearby data points to predict the value of a new point. It can capture local patterns that may not be apparent with global models. Clustering Algorithms: If you're interested in discovering inherent patterns in your data without a specific Y variable, clustering techniques like k-means or hierarchical clustering might be useful. Feature Engineering: Transform your features (X variables) to create new ones that might capture complex relationships more effectively. This might involve interactions, logarithmic transformations, or other functions. Time Series Analysis: If your data has a temporal component, time series techniques like ARIMA (AutoRegressive Integrated Moving Average) or LSTM (Long Short-Term Memory) networks could be useful. Dimensionality Reduction: Techniques like Principal Component Analysis (PCA) or t-SNE (t-Distributed Stochastic Neighbor Embedding) can help you visualize and analyze high-dimen
great answer, thanks ! when can i enroll in your classes ? :) cheers, Bill
«The mind is not a vessel to be filled but a fire to be kindled» Plutarch
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great answer, thanks ! when can i enroll in your classes ? :) cheers, Bill
«The mind is not a vessel to be filled but a fire to be kindled» Plutarch
Thanks Bill, had a good chuckle just now. :-D
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The redirects are usually to people who post questions relating to the CodeProject AI, and we point them to the [CodeProject.AI Discussions](https: //www.codeproject.com/Feature/CodeProjectAI-Discussions.aspx)[^] forum, rather than the general AI one here. And yes, I agree it can be confusing.
Thanks,, I made the suggestion as it is an AI related question.
