KNNClassifier #330
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KNNClassifier #330
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jamesb93
approved these changes
Jun 7, 2022
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| KNNClassifier is a supervised machine learning algorithm for classifying data points to learned categories. It uses an internal [KDTree](/reference/kdtree) to find the _k_ nearest neighbours of a point that needs classification (where _k_ is an integer >= 1). Whichever category, or "class", is most common among the neighbours is predicted as the category for that point. If an even number of `numNeighbours` is requested and there is a tie, the label with the closer point will be predicted. The parameter `weight` indicates whether or not the prediction should be weighted by the neighbours' distances. |
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| FluCoMa includes another object for classification, the [MLPClassifier](/reference/mlpclassifier), which also uses supervised learning for classification. The KNN object works quite differently from the MLP object, each having their strengths and weaknesses. The main differences to know are that: | ||
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| 1. the flexibility of the MLP objects make them generally more capable of learning complex relationships between inputs and outputs, |
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Would capitalise "The" in each instance
| FluCoMa includes another object for classification, the [MLPClassifier](/reference/mlpclassifier), which also uses supervised learning for classification. The KNN object works quite differently from the MLP object, each having their strengths and weaknesses. The main differences to know are that: | ||
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| 1. the flexibility of the MLP objects make them generally more capable of learning complex relationships between inputs and outputs, | ||
| 2. the MLP objects involve more parameters and will take much longer to `fit` (aka. train) than the KNN objects, and |
sounds good. while making this i imagined it but didn't go to implement. I think it could be a not to complicated extension of the kdtree gui. |
That's what I imagined as well. I'll make an issue and we can loop when we are less pressed? |
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