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- Step 1. Intersection over Union # def intersection_over_union(dt_bbox, gt_bbox): ---> return iou Step 2. Evaluate Sample We now have to evaluate the predictions of the model. To do this, we will write a function that will do the following: Take model predictions and ground truth bounding boxes and labels as inputs. For each bounding box from the prediction, find the closest bounding box among the answers. For each found pair of bounding boxes, check whether the IoU is greater than a certain threshold iou_threshold. If the IoU exceeds the threshold, then we consider this answer as True Positive. Remove a matched bounding box from the evaluation. For each predicted bounding box, return the detection score and whether we were able to match it or not. def evaluate_sample(target_pred, target_true, iou_threshold=0.5): # ground truth gt_bboxes = target_true['boxes'].numpy() gt_labels = target_true['labels'].numpy() # predictions dt_bboxes =…Please help with the ai question below, thanks Given a number of data samples (X, Class) in the attached file where each data sample consists of a variable X and a Class whose value is 1 or 2. (1) Using the given sample data, use the Gradient Descent algorithm to predict the logistic regression model (Note: the logistic regression model is NOT a regression model). (2) Using the logistic regression model as a solution to point (1) above, predict the Class of a sample that has a value of X = 5.6May I see the 5 samples from the Bayesian network and then using these 5 samples to answer the following query: P(S = s1|L = l1) =?
- 09.Issue 4 All through this issue, think about the accompanying setting: rng('default') % Set the irregular number generator to the default seed A first request autoregressive model (AR(1)) can be characterized in the accompanying way. y= Py.- + u. 1= 1,2.T. where y, indicates the worth of y at time r, p is a boundary and u, is an irregular variable that has a standard ordinary dispersion. When p= 1, the model is known as the irregular walk model. Expect that y, = 0 (when t=), and T=200. Utilize a for circle to produce 200 perceptions concurring this model when p=1 and p= 0.7.4 the task is to estimate two models1. Cobduglus (after taking log to convert it into log-linear)2. Estimate the linear model without logPlease help with the artificial intelligence question below thanks! Given a number of data samples (X, Class) in the attached file where each data sample consists of a variable X and a Class whose value is 1 or 2. (1) Using the given sample data, use the Gradient Descent algorithm to predict the logistic regression model (Note: the logistic regression model is NOT a regression model). (2) Using the logistic regression model as a solution to point (1) above, predict the Class of a sample that has a value of X = 5.6
- Create 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.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.)Method: Python Dataset: Census Income Source: https://archive.ics.uci.edu/ml/datasets/Adult Objective The objective of this task is to implement from scratch Decision Tree classification method to predict whether the incomes exceed $50K/yr based on census data. Thus, this is a binary classification problem. The training and test sets are pre-defined in the data set (i.e., in "adult.data" and "adult.test"). Requirements (1) Implement two DT models by choosing any two (2) split criteria from Information Gain, Gain Ratio, Gini Index and Variance. Note that you can use either binary-split or multiple-split. (2) Use (approximately) 2/3 records in "adult.data" for training, and 1/3 records in "adult.data" for post-pruning. (3) Report the accuracy of each model. (4) All DT models must be self-implemented. You CANNOT use any machine learning library in this task. (5) It is recommended that your implementation includes a "tree induction function", a "classification function" and a…
- Given a number of data samples (X, Class) in the attached file where each data sample consists of a variable X and a Class whose value is 1 or 2. - Using the given sample data, use the Gradient Descent algorithm to predict the logistic regression model (Note: the logistic regression model is NOT a regression model). - Using the logistic regression model as a solution to the question above, predict the Class of a sample that has a value of (X = 5.6)3You 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.Given U = {all attribute blocks}, design two sets A and B such that A union B equals to A