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- Develop a simple linear regression model (univariate model) using gradient descent methodfor experience-salary datasets as it is shown on the following table. Once you got the model, checkhow close the predicted values against the ground truth and calculate the total error (mean squareerror) and the accuracy R2.) Develop a simple linear regression model (univariate model) using gradient descent methodfor experience-salary datasets as it is shown on the following table. Once you got the model, checkhow close the predicted values against the ground truth and calculate the total error (mean squareerror) and the accuracy R2.No Experience Salary ($) Predicted3You are expected to train a Linear Regression model on any given data. After you have trained a linear regressor model. Apply Principal Component Analysis on the given dataset. Again, train a linear regressor model on the data with lesser dimensions.You are expected to write the code for the linear regression model, principal component analysis by using numpy and python.
- If you are implementing regularised linear regression and when you tested your hypothesis in a new dataset you found that it suffer from high variance. How can you rectify the model ? Give 3 SolutionsYou are provided with last year’s data showing which high school students chose standard or advanced coursework. The predictor variables include their writing score, math score, and science scores from previous years. Your task is to build a model that predicts if this year's incoming students are in advanced or standard coursework given the above predictor variables. Which model is suitable for this task? Linear regression k-means Clustering Logistic Regression Regression treeAs a data scientist, you are supposed to build a multivariate linear regression (MLR) model that might be used to predict the Stock Price Index (dependent variable) based on two independent variables namely Interest Rate and Unemployment Rate as they are shown in the mlr_dat dataset. Find the intercept b and the coefficient Wi. Compare and show side by side to see how close your MLR model prediction.
- Question 91. Consider the following training set of m=4 training examples: x y 0.1 0.6 1 1.5 0 0.5 3 3.5 Consider the linear regression model hθ(x)=θ0+θ1x. What are the values of θ0 and θ1 that you would expect to obtain upon running gradient descent on this model? (Linear regression will be able to fit this data perfectly.)Question 1 1)When our predictor variables have ranges and units that are quite different, it is pertinent to scale them before using them in a regression. Which of the following statements regarding scaling is FALSE? a)Normalisation is a scaling process which is inherently sensitive to outliers in the data. b)Standardisation is the process of squeezing a range of values to into the range [0,1]. c)Standardisation centres and scales a set of values such that they all have a mean of 0 and standard deviation of 1. d)Normalisation is the process of squeezing a range of values to into the range [0,1]. 2) The R-squared measure is said to take on a 'proportion' of some attribute associated with the model. What is that proportion? a)Proportion of observations used for training. b)Proportion of variance explained. c)Proportion of outputs correctly predicted. d)Proportion of predictor variables contributing to output.Which statements are true about LASSO linear regression? Group of answer choices has embedded variable selection by shrinking the coefficient of some variables to exactly zero. has one hyper-parameter lambda (The regularization coefficient) which needs to be tuned if there are multiple correlated predictors lasso will select all of them adds the L2 norm of the coefficients as penalty to the loss function to penalize larger coefficients
- We want to build a regression model and have many observations and many predictors. (a) From a computational point of view, which of these two model building algorithms are preferable: best subset selection or forward stepwise selection? (b) True or false and explain: Best subset selection will result in a smaller prediction error than forward stepwise selection because every model that is considered in forward stepwise selection is also considered in best subset seCreate Second Image Now that we have fit our model, which means that we have computed the optimal model parameters, we can use our model to plot the regression line for the data. Below, I supply you with x_fit and y_fit that represent the x- and y-data of the regression line, respectively. All we need to do next is ask the model to predict a z_fit value for each x_fit and y_fit pair by invoking the model's predict() method. This should make sense when you consider the ordinary least squares linear regression equation for calculating z_fit: ????=?̂ 0+?̂ 1????+?̂ 2????zfit=θ^0+θ^1xfit+θ^2yfit where ?̂ ?θ^i are the computed model parameters. You must use x_fit and y_fit as features to be passed together as a DataFrame to the model's predict() method, which will return z_fit as determined by the above equation. Once you obtain z_fit, you are ready to plot the regression line by plotting it against x_fit and y_fit. Any dataset would be great. I just want to understand it.Please help with artificial intelligence questuon below If given a number of data samples from pairs of values (x, y) namely {(0, 12), (1, 19), (2, 29), (3, 37), (4, 45)} (1) Make a data plot from the given sample where the vertical axis is the y value and the horizontal axis is the x value. (2) Given a simple regression model: y = a x + b where a, b are constant numbers. Use the given data sample and least square method to predict constants a and b from the regression model. (3) Using the predicted regression model in question (2) above, calculate the estimated value of y if the value of x = 7.