In ml model, there is an actual value in the dependent variable and the machine predicts a value according to the data. So bias is the distance/gap/difference between the actual value and predicted value. High bias means the gap is very much and low bias means the distance or gap is less or can be no gap or distance. Low bias model makes fewer assumptions about the form of the target function. A high bias makes more assumptions and for this reason it's unable to capture the important features of the dataset.

Linear Regression is a machine learning algorithm that is used to predict a quantitative target, with the help of independent variables that are modeled in a linear manner, to fit a line or a plane (or hyperplane) that contains the predicted data points. For a second, let's consider this to be the best-fit line (for better understanding). So, usually, points from the training data don't really lie on the best-fit line only, and that makes perfect sense because any data isn't perfect. That is why we are making predictions in the first place, and not just plotting a random line. The linear regression line cannot be curved in order to include all the training set data points, and hence is unable to capture an accurate relationship at times.

Linear Regression is a machine learning algorithm that is used to predict a quantitative target, with the help of independent variables that are modeled in a linear manner, to fit a line or a plane (or hyperplane) that contains the predicted data points. For a second, let's consider this to be the best-fit line (for better understanding). So, usually, points from the training data don't really lie on the best-fit line only, and that makes perfect sense because any data isn't perfect. That is why we are making predictions in the first place, and not just plotting a random line. The linear regression line cannot be curved in order to include all the training set data points, and hence is unable to capture an accurate relationship at times.

Moving forward, let's start with our basic imports: Let's say you want to make a model that is either a classification or regression based. How would you know which is the best model & which should you apply to your data set. In order to answer this, you need to fully understand what data you're trying to apply data science concepts to. My Cybersecurity data science project was a unbalanced classification problem. So I would decide to use a classification neural network model on the data.