k-NN | k-Nearest Neighboor algorithm with iris dataset | Machine Learning library

by aliihsancengiz C++ Version: Current License: No License

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kandi X-RAY | k-NN Summary

k-NN is a C++ library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Keras applications. k-NN has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

k-Nearest Neighboor algorithm with iris dataset

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k-NN has a low active ecosystem.

It has 5 star(s) with 0 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

k-NN has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of k-NN is current.

Quality

k-NN has no bugs reported.

Security

k-NN has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

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k-NN does not have a standard license declared.

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Without a license, all rights are reserved, and you cannot use the library in your applications.

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k-NN releases are not available. You will need to build from source code and install.

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k-NN Key Features

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k-NN Examples and Code Snippets

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Community Discussions

Trending Discussions on k-NN

Low accuracy in a k-NN algorithm from scratch

How can I visualize the test samples of k nearest neighbour classifier?

Draw figures using k-nn with different values of k

Add grid and change size in barh python

Supervised classification: plotting K-NN accuracy for different sample sizes and k values

Prediction with knn model from mlr library

Converting some columns into indicator variables after grouping by another column

install plugin for Open Distro

KNN using Caret package giving bad results compared to other methods

ValueError: Found input variables with inconsistent numbers of samples: [100, 300]

QUESTION

Low accuracy in a k-NN algorithm from scratch

Asked 2021-Feb-20 at 12:20

I am trying to implement a k-NN algorithm but it keeps resulting in very low accuracy values. There must be a logic error but I couldn't figure out where it is. The code is below:

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ANSWER

Answered 2021-Feb-20 at 12:20

( x <= K) should be replaced with x[1:K]. x's are rows containing order values of rows of eucdistcombined/mandistcombined correspondingly. ( x <= K) only gives indices which have value smaller than K, however indices of smallest distance values are needed. It should have been x[1:K] to obtain K-nearest-neighbors.

Source https://stackoverflow.com/questions/66286940

QUESTION

How can I visualize the test samples of k nearest neighbour classifier?

Asked 2020-Dec-15 at 17:23

I want to visualize 4 test samples of k-NN Classifier. I have searched it but I could not find anything. Can you help me with implementing the code?

Here is my code so far,

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ANSWER

Answered 2020-Dec-12 at 21:35

For that, you will basically need to reconstruct the KNN algorithm itself because it doesn't keep track of which "neighbors" were used to make prediction for a given sample.

How you are going to do that depends on what distance metric is being used by the KNN algorithm.

For example, you can define a function to fetch the nearest neighbors based on the L1 (Manhattan distance) like this:

Source https://stackoverflow.com/questions/65269382

QUESTION

Draw figures using k-nn with different values of k

Asked 2020-Sep-16 at 13:12

I want to plot figures with different value of k for k-nn classifier. My problem is that the figures seem to have same values of k. What I have tried so far, is to change the value of k in each run in the loop:

clf = KNeighborsClassifier(n_neighbors=counter+1) But all the figures seem to be for k=1

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ANSWER

Answered 2020-Sep-16 at 13:12

The reason why all the plots look the same is that you are simply plotting the test set every time instead of plotting the model predictions on the test set. You probably meant to do the following for each value of k:

Fit the model to the training set, in which case you should replace clf.fit(X_test, c_test) with clf.fit(X_train, c_train).
Generate the model predictions on the test set, in which case you should add c_pred = clf.predict(X_test).
Plot the model predictions on the test set, in which case you should replace c_test with c_pred in the scatter plot, i.e. use mglearn.discrete_scatter(X_test[:, 0], X_test[:, 1], c_pred, ax=ax[counter]) instead of mglearn.discrete_scatter(X_test[:, 0], X_test[:, 1], c_test, ax=ax[counter]).

I included the full code below.

Source https://stackoverflow.com/questions/63917715

QUESTION

Add grid and change size in barh python

Asked 2020-Sep-07 at 00:26

I am using matplotlib to draw horizontal plots. I want to add grids and change size of the plot to avoid overleap of the label. My code looks like this:

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ANSWER

Answered 2020-Sep-07 at 00:26

Specify the location of the legend by using plt.legend
- Making the figure larger won't necessarily make the legend fit better
Show the grid by using plt.grid()
plt.figure(figsize = (6,12)) didn't work, because the dataframe plot wasn't using this axes.

Source https://stackoverflow.com/questions/63769724

QUESTION

Supervised classification: plotting K-NN accuracy for different sample sizes and k values

Asked 2020-May-31 at 14:24

Hope you guys understand that it is hard to replicate something like this on a generic dataset.

Basically what I'm trying to do is perform K-NN with test and train sets of two different sizes for seven different values of k.

My main problem is that res should be a vector storing all the accuracy values for the same train-set size but it shows one value per iteration and this doesn't allow me to plot accuracy graphs as they appear empty.

Do you know how to fix this problem?

Data is available directly on R for free.

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ANSWER

Answered 2020-May-31 at 14:17

Your inner loop is supposed to fill the values in res, one per iteration. However, you seem to reset res at the end of each iteration of the loop. That's why it is not keeping any of the previous values.

These two lines need to be outside the inner loop (and inside the outer loop)

Source https://stackoverflow.com/questions/62117075

QUESTION

Prediction with knn model from mlr library

Asked 2020-May-21 at 14:25

How to make predictions with new data? I was only able to use the predict() function with the dataset. If I have x = 62.5, how do I predict the value of y?

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ANSWER

Answered 2020-May-21 at 14:25

You can use the newdata argument for that; see https://mlr.mlr-org.com/articles/tutorial/predict.html.

Source https://stackoverflow.com/questions/61925162

QUESTION

Converting some columns into indicator variables after grouping by another column

Asked 2020-May-10 at 13:26

I'm creating a k-nn model and I need to reorganize my dataset into a nice euclidean vectors.

My dataset looks like the one below

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ANSWER

Answered 2020-May-10 at 13:26

Try this:

Source https://stackoverflow.com/questions/61712434

QUESTION

install plugin for Open Distro

Asked 2020-Apr-11 at 09:26

Amazon Elasticsearch Service offers k-Nearest Neighbor (k-NN) search which can enhance search by similarity use cases.

https://aws.amazon.com/about-aws/whats-new/2020/03/build-k-nearest-neighbor-similarity-search-engine-with-amazon-elasticsearch-service/

I tried this official code that I found here...

https://github.com/opendistro-for-elasticsearch/k-NN

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ANSWER

Answered 2020-Apr-11 at 09:26

t2.small and t2.medium instance types are not supported. (It is not mentioned anywhere in the documentation.) It worked as expected when r5.large instance type was selected.

Source https://stackoverflow.com/questions/60578023

QUESTION

KNN using Caret package giving bad results compared to other methods

Asked 2020-Apr-10 at 22:25

I'm comparing a few different machine learning algorithms for automated essay scoring accuracy. The RMSE and RSquared values I'm getting for training sets are about 0.75 and 0.43 on average respectively. But for some reason when I run KNN using the same function framework I get RMSE=0.95 and RSquared=0.09. I'm not getting any error messages either so I don't know what's going wrong.

My data set is continuous and I'm performing regression on it.

Here is a snippet of my code:

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ANSWER

Answered 2020-Apr-10 at 22:25

My guess is you did not scale your independent variables for knn, this is crucial when your independent variables are on different scales. You can see a interesting discussion here:

Source https://stackoverflow.com/questions/61148617

QUESTION

ValueError: Found input variables with inconsistent numbers of samples: [100, 300]

Asked 2020-Feb-23 at 16:05

I was learning KNN from udemy. The dataset is downloaded from here.

When I try to run the following code:

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ANSWER

Answered 2020-Feb-23 at 12:29

Change line:

Source https://stackoverflow.com/questions/60362092

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You can download it from GitHub.

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