Sentiment Classification in Python In this notebook we are going to implement a LSTM model to perform classification of reviews. We are going to perform binary classification i.e. we will classify the reviews as positive or negative according to the Read more
Feature Selection Based on Univariate ROC_AUC for Classification and MSE for Regression Watch Full Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH What is ROC_AUC The Receiver Operator Characteristic (ROC) curve is well-known in evaluating classification performance. Owing to its superiority in dealing with imbalanced and Read more
Feature Selection using Fisher Score and Chi2 (χ2) Test Watch Full Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH What is Fisher Score and Chi2 ( χ2) Test Fisher score is one of the most widely used supervised feature selection methods. However, it selects each feature Read more
Feature Selection Based on Univariate (ANOVA) Test for Classification Watch Full Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH What is Univariate (ANOVA) Test The elimination process aims to reduce the size of the input feature set and at the same time to retain the class Read more
Feature Selection Based on Mutual Information (Entropy) Gain Watch Full Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH What is Mutual Information The elimination process aims to reduce the size of the input feature set and at the same time to retain the class discriminatory information Read more
Filtering method Watch Full Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH Unnecessary and redundant features not only slow down the training time of an algorithm, but they also affect the performance of the algorithm. There are several advantages of performing feature selection before training machine Read more
Feature Dimension Reduction Watch Full Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH What is LDA (Linear Discriminant Analysis)? The idea behind LDA is simple. Mathematically speaking, we need to find a new feature space to project the data in order to maximize classes separability Linear Read more
Watch Full Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH Linear Regression Let’s first understand what exactly linear regression is, it is a straight forward approach to predict the response y on the basis of different prediction variables such x and ε. . There is a Read more
Recursive Feature Elimination (RFE) Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH As it’s name suggests, it eliminates the features recursively and build a model using remaining attributes then again calculates the model accuracy of the model..Moreover how it do it train the model on all Read more
What is RNN Recurrent Neural Networks are the first of its kind State of the Art algorithms that can Memorize/remember previous inputs in memory, When a huge set of Sequential data is given to it. Recurrent Neural Networks are the Read more
Wrapping method Uses of Wrapping method Use combinations of variables to determine predictive power. To find the best combination of variables. Computationally expensive than filter method. To perform better than filter method. Not recommended on high number of features. Forward Read more
What is Regularisation? Regularization adds a penalty on the different parameters of the model to reduce the freedom of the model. Hence, the model will be less likely to fit the noise of the training data and will improve the Read more
What is Logistic Regression? Logistic Regression is a Machine Learning algorithm which is used for the classification problems, it is a predictive analysis algorithm and based on the concept of probability. Logistic regression is basically a supervised classification algorithm. In Read more
Principal Component Analysis(PCA) According to Wikipedia, PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values Read more
How does the KNN algorithm work? In KNN, K is the number of nearest neighbors. The number of neighbors is the core deciding factor. K is generally an odd number if the number of classes is 2. When K=1, then Read more
What is K-Mean Clustering? Machine Learning can broadly be classified into three types: Supervised Learning Unsupervised Learning Semi-supervised Learning K-means algorithm identifies k number of centroids, and then allocates every data point to the nearest cluster, while keeping the centroids Read more
What is Linear Regression? You are a real estate agent and you want to predict the house price. It would be great if you can make some kind of automated system which predict price of a house based on various Read more
What is it? A Random Forest is an ensemble technique which can have capable of performing both regression and classification tasks with the use of multiple decision trees and a technique called Bootstrap and Aggregation, commonly known as bagging. The Read more
How bagging works First of all we will try to understand what Bagging is from the following diagram: Let’s say we have a dataset to train the model, first we need to divide this dataset into number of datasets(atleast more Read more
What is Ensemble Learning? We can define Ensemble Learning in this way it uses multiple machine learning models or multiple set of models for the same algorithm which try to make a better prediction. Ensemble Learning model works by training Read more