In discriminative models, you have “less assumptions”, e.g,. in naive Bayes and classification, you assume that your p(x|y) follows (typically) a Gaussian, Bernoulli, or Multinomial distribution, and you even violate the assumption of conditional independence of the features. In favor of di...
Types of Naïve Bayes classifiers There isn’t just one type of Naïve Bayes classifier. The most popular types differ based on the distributions of the feature values. Some of these include: Gaussian Naïve Bayes (GaussianNB):This is a variant of the Naïve Bayes classifier, which is ...
The learning process here is monitored or supervised. Since we already know the output the algorithm is corrected each time it makes a prediction, to optimize the results. Models are fit on training data which consists of both the input and the output variable and then it is used to make p...
Naïve Bayes classifiers include multinomial, Bernoulli and Gaussian Naive Bayes. This technique is often used in text classification, spam identification and recommendation systems. Linear regression: Linear regression is used to identify the relationship between a continuous dependent variable and one ...
Gaussian Naive Bayes # Gaussian Naive Bayes from sklearn.naive_bayes import GaussianNB from sklearn.metrics import accuracy_score gaussian = GaussianNB() gaussian.fit(X, y) y_pred = gaussian.predict(X_test) gaussian_accy = round(accuracy_score(y_pred, y_test), 3) print(gaussian_accy) 0.78...
What the Naive Bayes classifier is actually doing behind the scenes to predict the probabilities of continuous data? It’s nothing but usage of probability density functions. So here Naive Bayes is generating a Gaussian (Normal) distributions for each predictor variable. The distribution is ...
Naive Bayes generally requires a small number of training data for classification.5. Logistic RegressionLogistic regression is a type of statistical algorithm that estimates the probability of occurrence of an event.Look at the following diagram. It shows the distribution of data points in the XY ...
Model-based clustering algorithms assume that the data is generated from a mixture of probability distributions. These algorithms attempt to find the best statistical model that represents the underlying data distribution. One popular model-based clustering algorithm is Gaussian Mixture Model (GMM). GMM...
Extensive knowledge of statistics, calculus or algebra to work withalgorithmsand an understanding of probability to interact with some of AI's most common machine learning models, including naive Bayes, hidden Markov and Gaussian mixture models. ...
A kernel function is a mathematical function used in the kernel trick to compute the inner product between two data points in the transformed feature space. Common kernel functions include linear, polynomial, Gaussian (RBF) and sigmoid. Kernel trick ...