Julia Data Structures The following are some of the most common data structures we end up using when performing data analysis on Julia: Vector(Array) – A vector is a 1-Dimensional array. A vector can be created by simply writing numbers separated by a comma in square brackets. If you add a semicolon, it will change the row. Vectors are widely used in linear algebra. Note that in Julia the indexing starts from 1, so if you want to access the first element of an array you’ll do A[1]. Matrix – Another data structure that is widely used in linear algebra, it can be thought of as a multidimensional array. Here are some basic operations that can be performed in a matrix Dictionary – Dictionary is an unordered set of key: value pairs, with the requirement that the keys are unique (within one dictionary). You can create a dictionary using the Dict() function. Notice that “=>” operator is used to link key with their respective values. You a...
The purpose of this research is to put together the 7 most common types of classification algorithms along with the python code: Logistic Regression, Naïve Bayes, Stochastic Gradient Descent, K-Nearest Neighbours, Decision Tree, Random Forest, and Support Vector Machine 1 Introduction 1.1 Structured Data Classification Classification can be performed on structured or unstructured data. Classification is a technique where we categorize data into a given number of classes. The main goal of a classification problem is to identify the category/class to which a new data will fall under. Few of the terminologies encountered in machine learning – classification: Classifier: An algorithm that maps the input data to a specific category. Classification model: A classification model tries to draw some conclusion from the input values given for training. It will predict the class labels/categories for the new data. Feature: A feature is an individual measurable property of...
Comments
Post a Comment