« Back Types of Regression Data Science Training in Pune Techniques to Know

By vikas  |  Data science and Analytics  |  On 6/19/2018 11:13:39 PM

Regression techniques are one of the most crucial part of training inData Science Training in Pune. Here are some interesting types of regression techniques that you need to know as a part of your job responsibilities:

1 – Stepwise regression

When working with statistical formats like R-square or t-stats, eliminating unwanted co-variates and retaining the meaningful ones for next steps becomes important. This technique comes in handy when there are multiple independent variables involved. This way, the number of predictor variables come down significantly. In turn, the prediction power of the technique enhances the analysis process.

2 – Polynomial regression

An expert in Data Science Training in Pune  can consider a regression equation as a polynomial regression if the power of indecent variables exceed the value of ‘1’. A typical graph is a curved one rather than a straight line. Make sure to avoid bringing in weird outcomes due to extrapolation when using higher polynomials.

3 – Logistic regression

For data that is binary in nature (only two answers possible: for e.g., ‘True/ False’, or ‘Yes/ No’), logistic regression comes in handy. It comes in handy for classification issues. It need not depict a linear relationship between dependent and independent variables. It is recommended to include all significant variables in this technique, to avoid over fitting and under fitting.

4 – Linear regression

This is perhaps on the best known regression techniques. For experts in Data Science Classroom Training in Pune, this is typically the first type people learn during their training. The nature of regression is linear and tracks continuous dependent variables and continuous/ discrete independent variables. Thus it seeks to ascertain a relationship between a dependent variable and one or more independent variables via a best fit straight line (typically termed as a regression line)

5 – Lasso regression

Lasso (Least Absolute Shrinkage and Selection Operator) seeks to use shrinkage in the regression calculation. Here data values are shrunk towards a central/ focal point. As per data science Course Pune experts, this type of regression works well with models that show increased levels of multicollinearity. It is also used in tasks where a part of the model selection needs to be automated.

To wrap up

These five regression techniques power up Din a significant way. The most effective way to leverage these is to know which technique to use to solve what kind of problem. Once you have mastered this, regression becomes a simpler challenge to solve.

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