AdaBoost, short for Adaptive Boosting, is a powerful ensemble method for classification in python. It is a meta-algorithm that combines multiple weak classifiers to form a strong one. The basic idea behind AdaBoost is to fit a sequence of weak learners (i.e., models that are only slightly better than random guessing) on repeatedly modified …
In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in Python programming: Compare Algorithms using IRIS DataSet. What should I learn from this recipe? You will learn: Compare Algorithms using IRIS DataSet. iris.data Compare Algorithms using IRIS …
In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in Python programming: Compare Algorithms using Diabetes DataSet. What should I learn from this recipe? You will learn: Compare Algorithms using Diabetes DataSet. pima-indians-diabetes.data Compare Algorithms using Diabetes …
Normalization is a process of adjusting the values of a variable so that it conforms to a standard scale. In python, there are several ways to normalize data. One common method is to use the Min-Max Scaler, which scales the values of a variable between a minimum and maximum value, typically 0 and 1. …
Standardizing data is a process of transforming the data so that it has a mean of 0 and a standard deviation of 1. This is often done to ensure that all variables in the data have the same scale and the same distribution, which is important for many machine learning algorithms. There are several …
Rescaling data is a technique used to standardize the range of independent variables or features of data. This is done in order to ensure that all features have the same scale and importance. Without rescaling, certain features may have an artificially larger impact on the final results due to their larger numeric values. There …
Scatter matrix plots, also known as pair plots, are a great way to visualize and understand the relationship between multiple variables in a dataset. These plots are created by plotting each variable against every other variable in a matrix format, with each cell in the matrix representing a scatter plot of two variables. To …
Density plots, also known as kernel density estimation plots, are a way to visualize the distribution of a continuous variable. They are similar to histograms, but instead of showing the frequency of data points in certain bins, they show the estimated probability density of the data. In other words, a density plot gives an …
Correlation Matrix Plots | Jupyter Notebook | Python Data Science for beginners | Data Visualisation
Correlation Matrix Plots A correlation matrix is a table that shows the correlation coefficients between multiple variables. Correlation matrix plots in Python are a way to visualize these correlation coefficients in a graphical format. In this essay, we will go over the steps needed to create correlation matrix plots in Python. The first step …
Box and Whisker Plots Box and whisker plots, also known as box plots, are a way to visualize the distribution of a dataset in Python. In this essay, we will go over the steps needed to create box and whisker plots in Python. The first step is to load the data that you want …