Seeking Guidance: Balancing Machine Learning and Mobile App Development as a BCA Student
I am currently in my second year pursuing a Bachelor of Computer Applications (BCA) at Christ University. After exploring various fields in technology, I have developed a keen interest in both mobile app development and machine learning.
How to Convert Date Formet
I was training the model in python the below is my code!
which data will be used for Football prediction model
I am planning to create a model to predict football match outcomes using various machine learning techniques. The columns of the collected data include Home, Away, Home Team Goals, Away Team Goals, Home Team Possession, Away Team Possession, Home Team Shots on Target, Away Team Shots on Target, etc. While it seems straightforward to use this data to predict match outcomes, these figures are only available after the match has ended. I believe we should use data that is available before the match, such as club value, points, and recent match results, to make predictions. Therefore, I want the columns of the training data to include both match data and pre-match data, and I want to make predictions using pre-match data. I am also curious about what this concept is called in machine learning.
Logistic Regression Different Cost Function Algorithm In Gradient Descent vs Cost Function Itself
Why is it in logistic regression that the cost function looks like this:
enter image description here
How is the performance of a model when its trained on highly unbalanced dataset
Let’s say we have 1000 samples of class-1 and 1000 samples of class-2. A model is traied on this data and we found the performance of the model is good. What happens to the performance of model when it is trained on a data with 10000 samples of class-1 and 90000 samples if class-2?
Does clustering actually reduce the number of rows in a dataset?
I am reading the book “grokking Machine Learning” by Luis G. Serrano and came across the following sentence:
Machine Learning Confusion Matrix Explanation of Matrix
`The following shows a few Confusion Matrices. The TP, FP, FN & TN are marked (using ChatGPT)
Wrong machine learning predictions at every run
I trained a machine learning model, and a few of the same rows (around 100 out of 1800) in my test set are giving wrong predictions at every run (10 times) with different seeds. Is there anything I should do about it e.g. throw it in the train set in exchange for some data from the train set to test set or leave it as it is?
Would passing correctly predicted images by a model back to the model for training purposess yield anything useful to us?
I do not have the required technical knowledge to try and test the metrics of a model to compare results and hypothesize, hence I am relying on the wisdom of the community.
I tried to look for answers however did not find anything useful (perhaps I phrased my question incorrectly).
Any guidance on the above topic would be appreciated.
In a generative model, how to factorize the likelihood of observing the data using the DAG dependencies?
In Machine Learning, considering a generative model represented by the following Direct Acyclc Graph (DAG):