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Overfitting

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Overfitting overview Overfitting of a model means that the model is too well trained i.e. it takes the noises and fluctuations in the training data as a set of observations to train the model, due to this the model’s ability to predict the output with the new data is affected. the model’s accuracy is highly affected. If the training data has many input factors it will have noise i.e. the randomness in the data which reduces the ability of the model to generalize. overfitted model will have high variance and low bias Variance is how much a model changes in response to the training data. Bias is the flip side of variance as it represents the strength of our assumptions we make about our data. bias and variance are forms of prediction error in machine learning. Overfit can be detected by dividing the data into training and test, if the model performs well on the training data than the test data, the model is overfitting How to avoid Overfitting? Finding overfitting in model ...
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Linear Regression

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Introduction: Before going inside linear regression,lets know what is regression.Regression is defined as the technique for determining the statistical relationship between two or more variables where the change in one variable(i.e dependent variable) is associated with,depends on change in one or more variables(independent variable).Machine Learning mostly uses regression to predict the outcome.In this article we will see about linear regression ,types and many other topics in linear regression What is a linear regression? linear regression gives the relationship between the variables i.e dependent variable and independent variable. The dependent variable is continuous and the independent variable may be continuous or discrete linear regression is classified into two types:                                    *s...
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Machine Learning Model Overview

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Introduction to ML Model. Performing machinelearning involves creating model, which is trained on some training data and then can process additional data to make predictions .  A machine learning  model can be a mathematical representation of a real-world application . To generate a machine learning model you will need to provide training data to a machine learning algorithm to learn from. The model finds the pattern in the training data and compares the pattern with the input test data and gives the output(predictions) . The algorithm with the patterns of the training data is called a model. There are several algorithms that can be used on the need Data handling preparing data files before applying machine learning algorithms took a whole lot of time. The data handling refers to the data cleaning and processing. It means handling missing values in the dataset, the row containing the missing values are dropped and another way in handling the missing val...
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Introduction to Machine learning

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What is machine learning? Machine learning is an application that can make the system to give the output from the past experiences . it is the process of making the system to learn from the past experiences without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly. Why machine learning? Machine learning models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results.  it’s possible to quickly and automatic...
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