Machine-Learning | Machine learning practice : kNN , decision tree | Machine Learning library

 by   Jack-Cherish Python Version: Current License: No License

kandi X-RAY | Machine-Learning Summary

kandi X-RAY | Machine-Learning Summary

Machine-Learning is a Python library typically used in Artificial Intelligence, Machine Learning applications. Machine-Learning has no bugs, it has no vulnerabilities and it has medium support. However Machine-Learning build file is not available. You can download it from GitHub.

:zap: Machine learning practice (Python3): kNN, decision tree, Bayesian, logistic regression, SVM, linear regression, tree regression
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              Machine-Learning has a medium active ecosystem.
              It has 7456 star(s) with 4964 fork(s). There are 263 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 2 open issues and 13 have been closed. On average issues are closed in 45 days. There are 2 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of Machine-Learning is current.

            kandi-Quality Quality

              Machine-Learning has 0 bugs and 0 code smells.

            kandi-Security Security

              Machine-Learning has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              Machine-Learning code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              Machine-Learning does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
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              Without a license, all rights are reserved, and you cannot use the library in your applications.

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              Machine-Learning releases are not available. You will need to build from source code and install.
              Machine-Learning has no build file. You will be need to create the build yourself to build the component from source.
              Machine-Learning saves you 1763 person hours of effort in developing the same functionality from scratch.
              It has 3900 lines of code, 170 functions and 33 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed Machine-Learning and discovered the below as its top functions. This is intended to give you an instant insight into Machine-Learning implemented functionality, and help decide if they suit your requirements.
            • Cross validation
            • Test the ridge test
            • Solve ridge regres
            • Calculates the rss error
            • Solves the smoothing problem
            • Return j random j random number
            • Clip alpha at aj
            • Tests the test set
            • SmoP algorithm
            • Plot lwlr regression
            • Loads the data set from a file
            • This function is used to test the handwriting class
            • Convert an image to vector
            • Returns a test text list
            • Main function for colic learn
            • Function to plot regression
            • Load a data set from a file
            • This test function is used to test colic test accuracy
            • Function to plot data set
            • Creates tree structure
            • Takes a folder_path in folder_path
            • Function to plot weights
            • Prune the tree
            • Solve the SMOP problem
            • Function to plot the classifer
            • Function to plot ROC curves
            • This function builds the Adam Gradient Estimator
            • Test for training digits
            Get all kandi verified functions for this library.

            Machine-Learning Key Features

            No Key Features are available at this moment for Machine-Learning.

            Machine-Learning Examples and Code Snippets

            Create the simplest Machine Learning backend-Create dummy model script
            Pythondot img1Lines of Code : 41dot img1License : Permissive (Apache-2.0)
            copy iconCopy
            from label_studio_ml.model import LabelStudioMLBase
            
            
            class DummyModel(LabelStudioMLBase):
            
                def __init__(self, **kwargs):
                    # don't forget to call base class constructor
                    super(DummyModel, self).__init__(**kwargs)
                
                    # you   
            Recipe - Machine Learning GUI
            Pythondot img2Lines of Code : 32dot img2License : Weak Copyleft (LGPL-3.0)
            copy iconCopy
                import PySimpleGUI as sg      
                  
                # Green & tan color scheme      
                sg.theme('GreenTan')      
                  
                sg.set_options(text_justification='right')      
                  
                layout = [[sg.Text('Machine Learning Command Line Parameters', fo  
            Create the simplest Machine Learning backend
            Pythondot img3Lines of Code : 7dot img3License : Permissive (Apache-2.0)
            copy iconCopy
              

            Community Discussions

            QUESTION

            How do I make a condition to execute a command once an element of numPy array achieves a certain value?
            Asked 2021-Jun-10 at 17:11

            I'm creating a machine-learning program to recognize images that are shown on webcam. I've used Google Teachable Machine to generate the model and it works fine.

            The matter I'm having issues with is printing the results of a prediction array, when an element of this array achieves a certain value (if it's equal to or more than 0.9 for an element, print a specific message).

            Let's say when element prediction[0] >= 0.9 I want to execute print("Up") as it recognizes the image of an arrow facing up or if element prediction[1] >= 0.9 I'd do a print("Down") etc.

            But when I try do that using the if statement I am presented with a

            ...

            ANSWER

            Answered 2021-Jun-10 at 17:11

            The problem is that your prediction has an "incorrect" shape when you're trying to check for each of the values. The following illustrates this:

            Source https://stackoverflow.com/questions/67924979

            QUESTION

            How can I access the best neural network model identifed through RandomizedSearchCV
            Asked 2021-Jun-08 at 15:25

            I am looking to extract and use (within the same Jupyter notebook) the model identified as the best model from RandomizedSearchCV for future fitting and graphing. Specifically, I am looking to re-fit the Keras Neural Network identified as the best so that I can plot the loss and accuracy against the same or other dataset.

            If I run the following code, I get the output I expect - the best score and the paramaters used in obtaining that score.

            ...

            ANSWER

            Answered 2021-Jun-07 at 21:35

            grid_result.best_estimator_ contains the refit estimator (since you've left the default value for the refit parameter), which is a fitted clone of your clf. That happens to be a pipeline object (with two steps) in your case; if you want to access the keras model, you can access it as though a dictionary:

            grid_result.best_estimator_['model'] will be a fitted KerasClassifier object. And those have the model attribute which contains the native keras object:

            grid_result.best_estimator_['model'].model

            Source https://stackoverflow.com/questions/67890007

            QUESTION

            How to create a TensorFloat for a shape that has unknown component?
            Asked 2021-Jun-07 at 16:08

            I have followed this example to bind input and output to a ONNX model.

            ...

            ANSWER

            Answered 2021-Jun-07 at 16:08

            When creating a tensor that will be used in conjuction with a model input feature that is defined with free dimensions (ie: "unk_518"), you need to specify the actual concrete dimension of the tensor.

            In your case it looks like you are using SqeezeNet. The first parameter of SqueezeNet corresponds to the batch dimension of the input and so refers to the number of images you wish to bind and run inference on.

            Replace the "unk_518" with the batch size that you wish to run inference on:

            Source https://stackoverflow.com/questions/67831406

            QUESTION

            Finding the correlation between variables using python
            Asked 2021-Jun-06 at 17:46

            I am trying to find the correlation of all the columns in this dataset excluding qualityand then plot the frequency distribution of wine quality.

            I am doing it the following way, but how do I remove quality?

            ...

            ANSWER

            Answered 2021-Jun-06 at 17:38

            QUESTION

            Removing strings within an html element duplicate content
            Asked 2021-Jun-05 at 13:22

            My initial HTML looks like this:

            ...

            ANSWER

            Answered 2021-Jun-05 at 13:22

            Perhaps you can try with regex in JS.

            Here's a codepen: https://codepen.io/johna138/pen/jOBxBLe

            Source https://stackoverflow.com/questions/67849792

            QUESTION

            How to build parameter grid with FeatureUnion?
            Asked 2021-Jun-01 at 19:18

            I am trying to run this combined model, of text and numeric features, and I am getting the error ValueError: Invalid parameter tfidf for estimator. Is the problem in the parameters synthax? Possibly helpful links: FeatureUnion usage FeatureUnion documentation

            ...

            ANSWER

            Answered 2021-Jun-01 at 19:18

            As stated here, nested parameters must be accessed by the __ (double underscore) syntax. Depending on the depth of the parameter you want to access, this applies recursively. The parameter use_idf is under:

            features > text_features > tfidf > use_idf

            So the resulting parameter in your grid needs to be:

            Source https://stackoverflow.com/questions/67794357

            QUESTION

            Difference between Tensorfloat and ImageFeatureValue
            Asked 2021-May-31 at 01:44

            When using the Windows-Machine-Learning library, the input and output to the onnx models is often either TensorFloat or ImageFeatureValue format.

            My question: What is the difference between these? It seems like I am able to change the form of the input in the automatically created model.cs file after onnx import (for body pose detection) from TensorFloat to ImageFeatureValue and the code still runs. This makes it e.g. easier to work with videoframes, since I can then create my input via ImageFeatureValue.CreateFromVideoFrame(frame). Is there a reason why this might lead to problems and what are the differences between these when using videoframes as input, I don't see it from the documentation? Or why does the model.cs script create a TensorFloat instead of an ImageFeatureValue in the first place anyway if the input is a videoframe?

            ...

            ANSWER

            Answered 2021-May-31 at 01:44

            Found the answer here.

            If Windows ML does not support your model's color format or pixel range, then you can implement conversions and tensorization. You'll create an NCHW four-dimensional tensor for 32-bit floats for your input value. See the Custom Tensorization Sample for an example of how to do this.

            Source https://stackoverflow.com/questions/67747696

            QUESTION

            what does the function iloc do in the iris dataset?
            Asked 2021-May-26 at 08:18

            Can someone explain what the bolded portions of this code. I have read the documentation for pandas and sklearn and it is still a bit hard to wrap my mind around it. I am wanting to modify this for my own data and would like to understand this a bit more.

            ...

            ANSWER

            Answered 2021-May-26 at 05:46

            .values is only returning the values of the data frame with the axis labels removed.

            .iloc uses integer-location based indexing.

            The .iloc portion of code is saying that we need the first 100 rows of only column 0 and 1 for our independent variable and only the first 100 rows of row 4 for our dependent variable. If this part is still confusing, I recommend that you look into slice notation. Quickly put, the slice notation on the .iloc simplifys to .iloc[start:stop].

            Original DataFrame:

            Source https://stackoverflow.com/questions/67698616

            QUESTION

            Fitting Keras model with Tensorflow datasets
            Asked 2021-May-25 at 03:42

            I'm reading Aurélien Géron's book, and on chapter 13, I'm trying to use Tensorflow datasets (rather than Numpy arrays) to train Keras models.

            1. The dataset

            The dataset comes from sklearn.datasets.fetch_california_housing, which I've exported to CSV. The first few lines look like this:

            ...

            ANSWER

            Answered 2021-May-25 at 03:42

            Just as the official docs for tf.keras.Sequential suggest, no batch_size needs to be provided when inputs are instances of tf.data.Dataset while calling tf.keras.Sequential.fit(),

            Integer or None. Number of samples per gradient update. If unspecified, batch_size will default to 32. Do not specify the batch_size if your data is in the form of datasets, generators, or keras.utils.Sequence instances (since they generate batches).

            In case of tf.data.Dataset, the fit() method expects a batched dataset.

            To batch the tf.data.Dataset, use the batch() method,

            Source https://stackoverflow.com/questions/67668873

            QUESTION

            IndexError when plotting pandas dataframe with subplots
            Asked 2021-May-17 at 04:54

            I'm working a beginner tutorial on this dataset here:

            http://archive.ics.uci.edu/ml/machine-learning-databases/undocumented/connectionist-bench/sonar/sonar.all-data

            I've loaded it like so:

            ...

            ANSWER

            Answered 2021-May-17 at 04:54
            • I don't know why, but using subplots=True with numeric column names seems to be causing the issue.
            • The resolution is to convert the column names to strings

            Source https://stackoverflow.com/questions/67563612

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install Machine-Learning

            You can download it from GitHub.
            You can use Machine-Learning like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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