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Results tagged with machine-learning
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user 54096
Machine Learning is a subfield of computer science that draws on elements from algorithmic analysis, computational statistics, mathematics, optimization, etc. It is mainly concerned with the use of data to construct models that have high predictive/forecasting ability. Topics include modeling building, applications, theory, etc.
4
votes
How to Justify Anomalies Detected by Unsupervised Anomaly Detection Models?
Your domain experts and other stakeholders are primary sources of information. Work them and work with them in order to find out what is an appropriate definition for "normal" and "abnormal". A key el …
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3
votes
Labelling a Time series dataset
Some feedback/tips/tricks/opinions here:
Problem setup
Including requirement analysis. Gotta decide how the system/solution should work, how to know ho how well we are doing, and then how to get there …
- 1,527
3
votes
Machine learning in audio?
There is now a youtube channel dedicated to Audio Machine Learning, The Sound of AI. There is also an associated Slack community for discussions.
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2
votes
Accepted
What happens to a machine learning technique (specifically Decision Tress and Logistic Regre...
There are no categorical value support in the decision trees used in scikit-learn. Either the values are just numbers, or they have been one-hot-encoded.
One-hot encoding with an unseen input value w …
- 1,527
2
votes
How does sklearn random forest use features in the form of 1D/2D array instead of a single v...
With RandomForest as it exists in scikit-learn, and practically all other implementations, there is no way to input structured data - be it 1D sequences or 2D matrices. All data must be transformed in …
- 1,527
2
votes
Predict how many days late or early someone will finish their work
If we assume that each task delivery is independent of eachother, and the process does not change a lot over time (stationary), we can treat this as a standard regression problem.
Since this is about …
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1
vote
I am trying to implement Isolation forest for anomaly detection but I am not able to underst...
According to this answer the range of output from scikit-learn IsolationForest decision_function is between -0.5 and 0.5, where smaller values mean more anomalous.
The predict function then applies a …
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1
vote
Accepted
Machine learning classification with time-domain signals how to ignore signal arrival time?
A common approach is what you suggest in 1. - apply time-shift as a Data Augmentation strategy. The augmentation is generally beneficial with deep learning models, and GPUs are fast so the compute tim …
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1
vote
Classify sensor data (multivariate time series) with Python's scikit-learn decision tree
For usage you need to flatten the 2D raw sensor data into 1D features. Below code demonstrates the basics.
What kind of feature engineering to apply for best predictive effect depends entirely on the …
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1
vote
Machine learning in audio?
For general sound, I recommend Computational Analysis of Sound Scenes and Events. Music and speech are popular sub-fields of audio that have a lot of literature dedicated to them, even before machine …
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1
vote
Why is pre and post silence important when collecting speech data?
Silence is often useful when segmenting the raw data into suitably sized samples for the machine learning methods. It is practical to run recording for several minutes at a time, but often the input t …
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1
vote
Testing if a sample fits into an existing cluster
The way to do anomaly detection with clustering is to compute the distance to each the fitted clusters. If a new sample has a distance above a certain threshold for all clusters, then it is considered …
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1
vote
could not broadcast input array from shape (13,160) into shape (13) while using sklearn norm...
Your MFCCs are a time-series, 2d representation. scikit-learn transformers like StandardScaler only works with 1d data (plus one dimension for the individual samples).
So you need to implement the sta …
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1
vote
Accepted
Should I train the "Unknown" class separately from the other classes
There are several ways of doing this. Examples are:
Binary classifier
Train a separate binary classifier for Known vs Unknown, using supervised learning. The Known data would come from your dataset, a …
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1
vote
What are the audio features to best describe a music?
The features you have selected are a good starting point, but are still (with the exception of tempo) quite "low level" compared to what might be most relevant for music recommendation systems.
The Es …
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