A support vector machine (SVM) is a supervised machine learning model which can be used for both classification and regression. But they have been extensively used for solving complex classification problems such as image recognition, voice detection etc. SVM algorithm outputs an optimal hyperplane that best separates the tags. The hyperplane is a boundary that…
Feature scaling is an important technique in Machine Learning and it is one of the most important steps during the preprocessing of data before creating a machine learning model. The reason to perform features scaling is to ensure one feature doesn’t dominate others. The two most important scaling techniques are…
In machine learning, bias, and variance are two critical sources of errors in models. 1. Bias: Definition: Bias is the error due to overly simplistic assumptions in the learning algorithm. High bias can cause the algorithm to miss the relevant relations between features and target outputs (underfitting), thereby leading to…
ROC curves and AUC are used to measure performance in machine earning. They are the most widely used evaluation metrics for checking any classification model’s performance. It tells how much the model is capable of distinguishing between classes. ROC (Receiver Operator Characteristic Curve) is a probability curve and AUC represents the…
Regularization is a process used to create an optimally complex model. A model should be as simple as possible. Ridge and Lasso regression are some of the simple techniques to reduce model complexity and prevent over-fitting which may result from simple linear regression. Linear regression is the simplest supervised machine learning…
Random Forest is a supervised algorithm and an ensemble learning method that constructs a multitude of decision trees during training and outputs the class that is the mode of the classes (classification) or the mean prediction (regression) of the individual trees. It is known for its flexibility and is used…
In Decision Tree, the major challenge is the identification of the attribute for the root node in each level. This process is known as attribute selection. There are two popular attribute selection measures: Gini Index Information Gain Gini Index calculates the amount of probability of a specific feature that is…
The decision tree algorithm is one of the most widely used algorithms in Machine Learning. It is a supervised learning algorithm. A decision tree uses a tree-like model to make predictions. It resembles an upside-down tree. A decision tree builds classification or regression models in the form of a tree…
Naive Bayes is a very popular Supervised Classification algorithm. This algorithm is called “Naive” because it makes a naive assumption that each feature is independent of other features. It is near to impossible to find such data sets in real life. Bayes’ theorem is the base for Naive Bayes Algorithm….
Logistic Regression is a supervised classification algorithm that is used to predict the probability of a categorical dependent variable using a given set of independent variables. It is a predictive analysis algorithm and based on the concept of probability. The most common use of logistic regression models is in binary classification problems. Some…