How to plot multiple graphs in Python?

how to plot multiple graphs in python?

How to plot multiple graphs with Python? First, you will need to define how many graphs you want. You can use the subplots() function to define the number subplots. Adding a new axis will create one more graph. A subplot can be created by adding more axes. The second step is to access each axis. … Read more

Understanding Boxplots Using Python

Boxplots Using Python

The shape of the data is an important aspect of the boxplot. The boxplot’s shape is used to represent the distribution of a population or sample in statistical analysis. This helps in analysing the data and drawing appropriate conclusions. Generally, the shape of the boxplot is symmetric. If the distribution contains more outliers that quartiles, … Read more

Data Science in Healthcare

Data Science in Healthcare

Introduction All the industries in the world today are run by data science. Being a vast field it has various applications relating to different aspects of life including healthcare, machine learning, road travel, sports and many others. Data science is growing its roots in almost each sector and it is one of those technologies that … Read more

Logistic Regression Detailed Overview

Logistic Regression Overview

Logistic regression is a statistical technique used to analyze data. It is a combination of probability and odds. The logistic regression model can handle any number of variables. The main difference between probability and odds is the scale. Logistic regression is capable of handling large numbers of variables, but it’s still simpler than other statistical … Read more

How to Implement Decision Trees in Python?

How to Implement Decision Trees in Python?

Scikit-learn includes an export_graphviz function that allows you to view the decision tree in a Jupyter notebook. To plot the results, you can use graphviz and pydotplus. You will need data to plot a decision tree when you create it. The dt package contains a number of functions that you can use to plot the … Read more

What is Model Complexity in Machine Learning?

What is Model Complexity in Machine Learning

When building AI algorithms, it is important to consider the complexity of the models. The complexity of a model is the number of training samples required to learn a target function. The algorithm will need more training samples to learn a new model. A strong variant of the learning algorithm takes the worst-case sample complexity … Read more

How to find a Probability Calculation using R

How to find a Probability Calculation using R

There are many options available to you if you want to do a probability calculation with R. There are many functions in R that can handle these calculations. Some functions deal with probabilities and likelihoods. To avoid memory underflow and overflow, use the log=TRUE option. The binomial distribution’s second parameter corresponds to the second value … Read more

A list of data extraction techniques

list of data extraction techniques

A list of data extraction techniques can help you manage your data. This type of processing requires careful preparation. Data must be cleaned and transformed before being used by software. Some sources of unstructured data are generated by humans or machines, while others come from Internet of Things (IoT) devices. These sources can be difficult … Read more

How to Reduce False Positives in Machine Learning

How to Reduce False Positives in Machine Learning

One of the most important things when working with AI or machine-learning is to reduce false positives. This can be solved by carefully selecting your dataset, and using the appropriate sampling techniques. A secondary classifier is recommended to reduce the chance of false positives being detected. This type of model is often more accurate, and … Read more