Machine Learning (Page 3)

SVM Kernels In practice, SVM algorithm is implemented with kernel that transforms an input data space into the required form. SVM uses a technique called the kernel trick in which kernel takes a low dimensional input space and transforms it into a higher dimensional space. In simple words, kernel convertsContinue Reading

Implementing SVM in Python For implementing SVM in Python we will start with the standard libraries import as follows − import numpy as np import matplotlib.pyplot as plt from scipy import stats import seaborn as sns; sns.set() Next, we are creating a sample dataset, having linearly separable data, from sklearn.dataset.sample_generatorContinue Reading

Support Vector Machine or SVM is one of the most popular Supervised Learning algorithms, which is used for Classification as well as Regression problems. However, primarily, it is used for Classification problems in Machine Learning. The goal of the SVM algorithm is to create the best line or decision boundaryContinue Reading

Science and technology have significantly helped the human race to overcome most of its problems. From making people fly in the air to helping them in managing traffic on roads, science has been present everywhere.  Not even a single field is there, where science isn’t involved. From live-saving machinery toContinue Reading

Logistic regression is one of the most popular Machine Learning algorithms, which comes under the Supervised Learning technique. It is used for predicting the categorical dependent variable using a given set of independent variables. Logistic regression predicts the output of a categorical dependent variable. Therefore the outcome must be aContinue Reading

What is the Classification Algorithm? The Classification algorithm is a Supervised Learning technique that is used to identify the category of new observations on the basis of training data. In Classification, a program learns from the given dataset or observations and then classifies new observation into a number of classesContinue Reading

What is Regularization? Regularization is one of the most important concepts of machine learning. It is a technique to prevent the model from overfitting by adding extra information to it. Sometimes the machine learning model performs well with the training data but does not perform well with the test data. It meansContinue Reading

RMSE: Root Mean Square Error is the measure of how well a regression line fits the data points. RMSE can also be construed as Standard Deviation in the residuals.Consider the given data points: (1, 1), (2, 2), (2, 3), (3, 6).Lets break the above data points into 1-d lists.Input : xContinue Reading

The Mean Squared Error (MSE) or Mean Squared Deviation (MSD) of an estimator measures the average of error squares i.e. the average squared difference between the estimated values and true value. It is a risk function, corresponding to the expected value of the squared error loss. It is always non – negative and valuesContinue Reading