Fork 8. The code for classification function in python is as follows, The validation function accepts validation data and predicts the accuracy of model on the data set, The essential components of Machine learning framework are. Then we slice the dataset to 80% and store it in train_data and the remaining 20% in test_data . Current function value: 0.573147\n". Just to have a proper structure, we have put all the functions in a single combine function. In the present application gradient based methods are used for minimization. We plot the data points in two-dimensional space (petal length and petal width). Credit Card Fraud Detection : How to handle Imbalanced Dataset function applies logistic function to a real valued input vector x, \( \begin{eqnarray*} P(Y=i|x, W,b) =\frac {e^{W_i x + b_i}}{\sum_j e^{W_j x + b_j}} \\ \end{eqnarray*}\), softmax function for multi class logistic regression, function predicts the probability of input vector, the output y is MX1 vector (M is no of classes), function returns the lables corresponding to the input y, function classifies the input vector x into one of output lables, input is NXD vector then output is NX1 vector, validation function to test the accuracy of model, function computes the negative log likelyhood over input dataset, params is optional argument to pass parameter to classifier, Useful in cases of iterative optimization routines for function evaluations, \(L(\theta,\mathcal{D}) \text{with respect to} \theta ,\partial{\ell}/\partial{W} \text{ and } \partial{\ell}/\partial{b}\), function to compute the gradient of parameters for a single data sample, function to compute the gradient of loss function over all input samples, params is optional input parameter passed to the classifier, which is, useful in cases of iterative optimization routines,added for compatiblity with, setting the training data in LogisticRegression class, getting initial parameter from LogisticRegression class, Executing nonlinear conjugate gradient descent optimization routines .", the function performs training for logistic regression classifier """, set the training and validation datasets""", pass the cost,gradient and callback functions""", Last Visit: 31-Dec-99 19:00 Last Update: 7-Nov-22 12:46, http://deeplearning.net/data/mnist/mnist.pkl.gz, https://github.com/pi19404/OpenVision/tree/master/ImgML/PyVision/LogisticRegression.py. Instantly share code, notes, and snippets. # test sigmoid function for a range of values. "cols_to_keep = ['admit', 'gre', 'gpa']\n". Sklearn: Sklearn is the python machine learning algorithm toolkit. Obtained values are shown in the screenshot below. Raw. linear_model: Is for modeling the logistic regression model. Clone via HTTPS Clone with Git or checkout with SVN using the repositorys web address. As we know, logistic regression can be used for classification problems. Gradient evaluations: 171 Logistic Regression: It works on same concept of Linear Regression but it is applicable when input X is continuous and the output Y to be predicted is descrete such as (yes,No), (Male,Female). "# keep only what we need for making predictions\n". After that, making predictions on our testing dataset. Thank you for your precious time.And I hope you like this tutorial. 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RURg0aBB+85vf4P3338eaNWu6vMfQ+CsrK8OYMWMQGRmJqKiobu3b0TeWTPWh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\nffp0nDlzxuHHPJduzIhg4X3FN0xZAJ7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"".
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