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O(f(n))on the left might be referring to Big-O notation, but I'm not sure if you're working in that space of work. But even so within Big-O notation, I'm not sure what the lowercase O is referring to. What area of work is this equation showing up in?
O(f(n))) is equivalent to
f(n)itself. This might make sense in the right context, but right here, it kinda doesn't mean much. I hope the notation explanation can lead you to the right direction. You can read more about time complexities here https://en.wikipedia.org/wiki/Time_complexity#Table_of_common_time_complexities
I have a dataset which is for binary classification ( or at least we are approaching it from a binary classification perspective )
There are a total of 2.5 million rows, with label 0 belonging to around 220000 (2.2 million) rows and label 1 belonging to around 321000 (0.3 million) rows , there are around 45 features.
The imbalance approaches a ratio of around 1 : 7
My problem is very straightforward, even WITHOUT any data preprocessing if i try to classify the data
the classification algorithms, no matter what parameters are set, give around 99% in ALL performance metrics ( accuracy, precision, recall, f1 score etc )
This would probably suggest a bad case of overfitting but i am not sure, feel free to explain and add your opinion to what could be the reason
I tried to visualize the graph using TSNE and saw that the entire data is shaped like an ellipse and there is heavy overlap between both the lables. This means that (1) data is badly imbalanced (2) data is badly overlapped , i highly doubt i can use anomaly detection there as all the 'anomalies' (label 1) are sitting close with the 'normal' (label 0) data
any suggestions on how i should proceed ?
sudo mysql_secure_installation Securing the MySQL server deployment. Enter password for user root: Error: Can't connect to local MySQL server through socket '/var/run/mysqld/mysqld.sock' (2)
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