Think Logically
Heteroscedasticity refers to the condition in which the variability of a variable is unequal across the range of values of a second variable that predicts it. A scatterplot of these variables will often create a cone-like shape, as the scatter (or variability) of the dependent variable (DV) widens or narrows as the value of the independent …
Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. It works for both categorical and continuous input and output variables. It is a non-parametric algorithm. It uses the following algorithms. 1- ID3: ID3 uses Entropy and Information gain to construct a decision tree. 2- Gini Index (, if we select two items from a population …
KNN is another supervised classification technique, which makes no assumption of distribution of data. It is a non-parametric algorithm.It is the simplest technique of classification, the algorithm used for this technique is as: Algorithm Let m be the number of training data samples. Let p be an unknown point. Store the training samples in an …
LSTM it a type of Recurrent neural network, Various variants of RNNs have been around since the 1980s but were not widely used until recently because of insufficient computation power and difficulties in training. Since the invention of architectures like LSTM in 2006 we have seen very powerful applications of RNNs. In 1997 by Hochreiter & …
CNN stands for convolutional neural network. It is mostly used for developed for object recognition tasks such as handwritten digit recognition. There are four types of layers in a Convolutional Neural Network: 1. Convolutional Layers. 2. Pooling Layers. 3. Fully-Connected Layers. 4.Dropout layers. CNN is just a function which operates on another function in the below …
The Naive Bayes Classifier technique is based on the so-called Bayesian theorem and is particularly suited when the dimensionality of the inputs is high. Naive Bayes is a classi cation algorithm for binary and multiclass classi cation problems. The technique is easiest to understand when described using binary or categorical input values. It is called naive Bayes or …