Can data about workplace absenteeism allow us to predict which employees are smokers? The k-NN algorithm is a supervised learning technique in classification problems. Our goal is to predict a label by developing a generalized model we can apply to previously unseen data.

The link above will send you to a. This will return a Pandas DataFrame for us to work with. As always, we want to get very well acquainted with our new bundle of data joy. We want to see what kind of features we are working with, check data types, and look for missing values in our records. This will generate a quick report for us to view, with a generated line break to easily separate each element of the report. As you can see, we have some interesting records that may allow us to make predictions about the employees.

Finally, we can see that our dataset is entirely comprised of int64 values, and none are missing. Interesting, it seems there might be some value here. Overview The k-NN algorithm is a supervised learning technique in classification problems. Exploratory Data Analysis As always, we want to get very well acquainted with our new bundle of data joy. Shape We have a total of 21 columns, and observations.

Columns As you can see, we have some interesting records that may allow us to make predictions about the employees. Info Finally, we can see that our dataset is entirely comprised of int64 values, and none are missing. Selecting Features Here is a simple method to input a desired column name and a related dataset.

But, can we do better? Plotting set plot style plt. Full Code from sklearn.The first exercise concerns k-nearest-neighbor kNN algorithm. There are many good sources describing kNN, so I will not take up much time or space here feel free to skip to the code below. Euclidean distance is calculated using Pythagorean formula scaled from 2 to N dimensions. Whatever label is most frequently encountered serves to classify our input point.

In the example from MLA, we have data about dates, each having three dimensions: number of miles traveled, grams of ice cream eaten, and hours of computer games played per month. Each datapoint has a label: how much our client liked her date on a scale from 1 to 3. Our goal is to take an input point a potential date and predict how well liked he will be by finding his k nearest neighbors from the training set.

Again, if you find the above description too sparse, there are many better tutorials elsewhere. My Julia and Python 3. Next, we want to normalize the values to ensure that no dimension influences our distance calculation more than others:. Now we are ready for the kNN function.

The function itself uses Distances package, which I imported in the first code block above. Distance calculation happens on line 4; lines 7—9 enter the k nearest distances into a dictionary. Otherwise, line 11 returns the most frequently encountered label.

Remember, the first time you import Gadfly package, as well as the first time you plot anything using it, will take a while.

Similar code in Python runs for about 0. Note: I got some help from StackOverflow gurus to optimize my function a bit. Apparently, list comprehensions slow down Julia quite a bit. Re-writing them as for loops sped up my code about twice.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again.

If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. K-Nearest Neighbors algorithm or k-NN for short is a non-parametric method used for classification and regression. The output depends on whether k-NN is used for classification or regression:. In k-NN classification, the output is a class membership. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors k is a positive integer, typically small.

In k-NN regression, the output is the property value for the object. This value is the average of the values of its k nearest neighbors. Reference Wikipedia. The training and test data is of format:. Where r,g,b is understandably the red, green, blue values of each pixel. There are totally features whew!!

The main concern with optimizing the KNN classifier is to select the right number of neighbors K and the distance function to be considered. Given the size of the input training set about and the feature vectorthe training for each test set will take a large amount of time. Hence, we reduced the feature set by converting them into pixel values.

These are single integers formed by shifting 8 bits from initial red value adding green value and again shifting 8 bits and adding the blue value. By this method there is almost no loss in information and the feature vector is effectively reduced to This did reduce time a lot than the full vector, the shifting and adding values did incur an overhead and strangely it gave us very bad results on accuracy of the classifier, maybe due to the drastic differences.

In terms of values of K When we tried picking very small values for K that is 1 or 2 then the knn classifier was over fitting the dataset. Because it was trying to build individual data points with their own individual decision boundaries. So, the classifier was performing better on training dataset that is was giving better accuracies on it whereas on test dataset the accuracy was getting reduced.K-Nearest Neighbors Algorithm aka kNN can be used for both classification data with discrete variables and regression data with continuous labels.

The algorithm functions by calculating the distance Sci-Kit Learn uses the formula for Euclidean distance but other formulas are available between instances to create local "neighborhoods". K-Nearest Neighbors functions by maximizing the homogeneity amongst instances within a neighborhood while also maximizing the heterogeneity of instances between neighborhoods. One neat feature of the K-Nearest Neighbors algorithm is the number of neighborhoods can be user defined or generated by the algorithm using the local density of points.

The Scikit—Learn Function: sklearn. For this implementation I will use the classic 'iris data set' included within scikit-learn as a toy data set.

Personally, I had no idea what a sepal was so I looked up some basic flower anatonmy, I found this picture helpful for relating petal and sepal length. Flower Anatomy:. This plot indicates a strong positive correlation between petal length and width for each of the three flower species. In this example we're using kNN as a classifier to identify what species a given flower most likely belongs to, given the following four features measured in cm : sepal length sepal width petal length petal width.

We use our data to train The kNN Classifier.

## Machine Learning: Predicting Labels Using a KNN Algorithm

Once the neighborhoods are defined, our classifier will be able to ingest feature data petal and sepal measurements on flowers it has not been trained on and determine which neighborhood it is most homogenous to. We can determine the accuracy and usefulness of our model by seeing how many flowers it accurately classifies on a testing data set.

Here's a graphical representation of the classifier we created above. As we can see from this plot, the virgincia species is relatively easier to classify when compared to versicolor and setosa.

The number of neighbors to implement is highly data-dependent meaning optimal neighborhood sizes will differ greatly between data sets. It is important to select a classifier which balances generalizability precision and accuracy or we are at risk of overfitting.

For example, if we pick a classifier which fits the data perfectly we will lose the ability to make generalizable inferences from it this would look like the 'low accuracy', 'high precision' scenario below because our model is very good at predicting training data but misses completely when presented with new data.

Best practice is to test multiple classifiers using a testing data set to ensure we're making appropriate trade-offs between accuracy and generalizability. Ernest Tavares III. This may take a moment. Petal Length vs Sepal Width plt. Predicted Species: ['versicolor'] Options: ['setosa' 'versicolor' 'virginica'] Probabilities: [[ 0.Any machine-learning algorithm could be used to classify the test set based on the classification model determined by the training set.

In this post, we discuss about how the KNN algorithm could be used to label these digits. A python script is used to run this algorithm on the test and training sets. No inbuilt machine learning python packages are used in the program for learning purposes. KNN KNN is a lazy learning algorithm, used to label a single test sample of data based on similar known labeled examples of data.

The following image from Wikipedia gives a visual example of how the KNN works. We use the Euclidean distance formula:. A small subset of MINST data of handwritten gray scale images is used as test dataset and training dataset.

Each of the images is given in the format of comma-separated pixel values in gray scale. The training data has the first value as the labeled value of the image. So, how do we determine the Euclidean distance between two such images? We do that by determining the difference between each of corresponding pixels of the images and squaring them. So, if the two images were exactly the same, then the squared distance would be 0.

For images, which are similar, we would expect this distance to be as small as possible. A digit class is defined with possibility to initialize with label and pixel array data. A distance function is defied to determine the Euclidean distance between two digits, by calculating the difference in pixel values between the digits. For every row in the data set, a new Digit class is defined.

The digit is then appended to an array and returned. The first row is ignored as it has the column labels and not the actual data. For every test set of data, we loop through the training set of data; we find the distance of the test digit image from each image in the training image based on the Euclidean formula.In my previous article i talked about Logistic Regressiona classification algorithm. K Nearest Neighbors is a classification algorithm that operates on a very simple principle.

It is best shown through example! Imagine we had some imaginary data on Dogs and Horses, with heights and weights. Training Algorithm:. This cleaner cut-off is achieved at the cost of miss-labeling some data points. You can read more about Bias variance tradeoff. Because the KNN classifier predicts the class of a given test observation by identifying the observations that are nearest to it, the scale of the variables matters. Any variables that are on a large scale will have a much larger effect on the distance between the observations, and hence on the KNN classifier, than variables that are on a small scale.

For every value of k we will call KNN classifier and then choose the value of k which has the least error rate. We were able to squeeze some more performance out of our model by tuning to a better K value. The above notebook is available here on github. Share: Twitter Facebook. Hardik Jaroli. Share it. Facebook Twitter Reddit Linkedin Email this. Related Posts. Online Courses. Connect with Us.While the individual man is an insoluble puzzle, in the aggregate he becomes a mathematical certainty.

You can, for example, never foretell what any one man will do, but you can say with precision what an average number will be up to. Individuals vary, but percentages remain constant. So says the statistician. A real statistician would go through the pros and cons of each, but alas, I am not a real statistician.

So instead, I write a witty introduction and move on! A quick note: the full code can be viewed here. Previous blog post is here. The idea is to plot each song in your library on a map. In this case, we would simply compute an x,y coordinate for each song, then plot all the songs on a map.

The second step is to take a new song, not in our corpus, and plot it on our map. Finally, we find the k -closest neighbors and find out the mode most frequently occurring song type.

**How kNN algorithm works**

We want to leverage all the words at once, not just two features of the song. A song with different words would yield different dimensions! And it would just be a bunch of 0s and 1s. I can barely think in 3 dimensionslet alone !

Cosine mother-f-ing similarity if you thought this blog was SFW, you forgot how desperate I am for attention. Cosine similarity measures the similarity between two non-zero vectors by taking the dot product over the magnitude of those two vectors:. The results range from -1, meaning exact opposite, to 1, meaning exactly the same. Consider the following sentences:. Basically, the more overlap in words, the bigger the numerator gets which increase the similarity.

Now that we have a similarity measure, the rest is easy! All we do is take our new song, compute the cosine similarity between this new song and our entire corpus, sort in descending order, then grab the top and take the mode of those. Starting with the training function:. I know all of you fans at home are screaming at your computer screens right now.

That first line, the TfidfVectorizerit does take all of our sentences and turn them into the tf-idf feature vectors for us.

### K-nearest neighbor exercise in Julia

The next line goes through our training data, and transforms it into a beautiful corpus. The predict function takes in a song that has already been converted into a feature vectorand outputs a category. The first line of this function takes the cosine similarity between the new song and our training corpus. We then sort the list and take the top results. We then use a counter to find the mode, and return the first result!

I hope this code helps you design your own kNN. The next steps here would be to calibrate appropriate s and perhaps exploring other similarity measures. After reading my previous blog post on CLV, you send off a data request to your data analytics guy asking him to pull the January subscribers and plot t Ross Hochwert I'm a consultant with a passion for data science.

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