title

Tutorial 48- Naive Bayes' Classifier Indepth Intuition- Machine Learning

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Guys there were some issue in the previous video. So I have reuploaded it. Sorry for the trouble.
In probability theory and statistics, Bayes' theorem describes the probability of an event, based on prior knowledge of conditions that might be related to the event
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{'title': "Tutorial 48- Naive Bayes' Classifier Indepth Intuition- Machine Learning", 'heatmap': [{'end': 391.978, 'start': 332.851, 'weight': 0.765}, {'end': 847.558, 'start': 791.945, 'weight': 0.869}], 'summary': "Delves into naive bayes' classifier, explaining its connection to bayes theorem and its role in machine learning classification problems. it covers understanding machine learning features, probability computation, and the application of bayesian theorem in a real-world scenario with detailed probability calculations and implementation of naive bayes classifier using a dataset of 14 records.", 'chapters': [{'end': 68.071, 'segs': [{'end': 54.22, 'src': 'embed', 'start': 26.423, 'weight': 0, 'content': [{'end': 32.506, 'text': 'you know, whenever there is a classification data set in machine learning, you can basically use this particular algorithm to solve it.', 'start': 26.423, 'duration': 6.083}, {'end': 37.009, 'text': 'and remember, the base of nape bias classifier is Bayes theorem.', 'start': 32.506, 'duration': 4.503}, {'end': 46.652, 'text': 'so this particular formula that I have actually displayed over here is called as Bayes theorem and I hope yesterday I have actually discussed about this.', 'start': 37.009, 'duration': 9.643}, {'end': 47.893, 'text': 'if you have not seen that,', 'start': 46.652, 'duration': 1.241}, {'end': 54.22, 'text': 'just go and see my previous tutorial in my complete machine learning playlist or just see my video which was uploaded yesterday.', 'start': 47.893, 'duration': 6.327}], 'summary': 'Use bayes theorem for classification in machine learning.', 'duration': 27.797, 'max_score': 26.423, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jS1CKhALUBQ/pics/jS1CKhALUBQ26423.jpg'}], 'start': 11.135, 'title': 'Nape bias classifier and bayes theorem', 'summary': 'Introduces the concept of nape bias classifier, based on bayes theorem, used for classification problems in machine learning, and explains how bayes theorem is applied in a classification problem.', 'chapters': [{'end': 68.071, 'start': 11.135, 'title': 'Nape bias classifier and bayes theorem', 'summary': 'Introduces the concept of nape bias classifier, which is based on bayes theorem and is used for classification problems in machine learning, and explains how bayes theorem is applied in a classification problem.', 'duration': 56.936, 'highlights': ['The Nape Bias Classifier is based on Bayes theorem and is used for solving classification problems in machine learning, as mentioned by Krishnayak.', "Bayes theorem is the foundation of the Nape Bias Classifier, and it is derived by conditional probability, explained in Krishnayak's previous tutorial."]}], 'duration': 56.936, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jS1CKhALUBQ/pics/jS1CKhALUBQ11135.jpg', 'highlights': ['The Nape Bias Classifier is based on Bayes theorem and is used for solving classification problems in machine learning, as mentioned by Krishnayak.', "Bayes theorem is the foundation of the Nape Bias Classifier, and it is derived by conditional probability, explained in Krishnayak's previous tutorial."]}, {'end': 417.313, 'segs': [{'end': 158.679, 'src': 'embed', 'start': 113.237, 'weight': 3, 'content': [{'end': 116.039, 'text': 'This particular base theorem formula is there.', 'start': 113.237, 'duration': 2.802}, {'end': 119.981, 'text': 'Right We have to change this based on our data set.', 'start': 116.079, 'duration': 3.902}, {'end': 121.861, 'text': 'Now, how does it actually change?', 'start': 120.401, 'duration': 1.46}, {'end': 130.083, 'text': 'Now understand, guys, suppose in my X record, which is my dependent features, I have this many features, suppose X1, X2, X3, X4 till Xn, right?', 'start': 122.261, 'duration': 7.822}, {'end': 132.184, 'text': 'So this may be one record.', 'start': 130.103, 'duration': 2.081}, {'end': 135.585, 'text': 'And for that record, I may have one output Y right?', 'start': 132.624, 'duration': 2.961}, {'end': 142.968, 'text': 'Now, if I consider a classification problem, if I have given these all features, I need to compute the value Y.', 'start': 137.003, 'duration': 5.965}, {'end': 146.951, 'text': 'And in this specific case, it will be a classification problem.', 'start': 142.968, 'duration': 3.983}, {'end': 156.478, 'text': 'Suppose I say that this is my features like height, weight, and I have to categorize whether this particular record is obese or not obese.', 'start': 147.331, 'duration': 9.147}, {'end': 158.679, 'text': 'So this may be the independent feature of a person.', 'start': 156.678, 'duration': 2.001}], 'summary': 'Explains how to change base theorem formula based on dataset and features, for a classification problem.', 'duration': 45.442, 'max_score': 113.237, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jS1CKhALUBQ/pics/jS1CKhALUBQ113237.jpg'}, {'end': 219.167, 'src': 'embed', 'start': 185.376, 'weight': 0, 'content': [{'end': 186.876, 'text': 'Now this particular feature is given.', 'start': 185.376, 'duration': 1.5}, {'end': 190.237, 'text': 'I have to compute the probability of Y.', 'start': 186.876, 'duration': 3.361}, {'end': 193.218, 'text': 'And the probability of Y is basically a classification problem.', 'start': 190.237, 'duration': 2.981}, {'end': 195.999, 'text': 'It may be yes or no, it may be multi-classification.', 'start': 193.738, 'duration': 2.261}, {'end': 199.619, 'text': "I'll also be discussing about multi-classification in the upcoming videos.", 'start': 196.019, 'duration': 3.6}, {'end': 202.72, 'text': 'But right now let me just consider a binary classification problem.', 'start': 200.039, 'duration': 2.681}, {'end': 206.121, 'text': 'So this Y may be yes or no, something like that.', 'start': 203.36, 'duration': 2.761}, {'end': 210.742, 'text': 'Now after doing this I will try to equate this particular equation.', 'start': 206.98, 'duration': 3.762}, {'end': 219.167, 'text': 'Now probability of B given A because in order to compute this we are dependent on this value this value and this value.', 'start': 211.303, 'duration': 7.864}], 'summary': 'Discussing computation of probability of y for binary classification', 'duration': 33.791, 'max_score': 185.376, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jS1CKhALUBQ/pics/jS1CKhALUBQ185376.jpg'}, {'end': 314.302, 'src': 'embed', 'start': 278.106, 'weight': 1, 'content': [{'end': 279.467, 'text': 'Right Pretty much simple.', 'start': 278.106, 'duration': 1.361}, {'end': 285.951, 'text': "We are just trying to convert this equation such that we'll be able to solve this particular machine learning problem statement.", 'start': 279.947, 'duration': 6.004}, {'end': 288.372, 'text': 'Okay Specifically classification problem statement.', 'start': 286.351, 'duration': 2.021}, {'end': 293.316, 'text': 'Now, in the next statement, you can see that probability of Y.', 'start': 289.013, 'duration': 4.303}, {'end': 298.464, 'text': "I'm taking it over here and since this is a multiplication problem, it depends on the number of features.", 'start': 293.316, 'duration': 5.148}, {'end': 300.006, 'text': 'okay, 1 to n.', 'start': 298.464, 'duration': 1.542}, {'end': 303.571, 'text': 'I can also write this as pi 1 I is equal to 1 to n.', 'start': 300.006, 'duration': 3.565}, {'end': 305.434, 'text': 'probability of X of I given Y.', 'start': 303.571, 'duration': 1.863}, {'end': 314.302, 'text': 'Now this I will be iterating from 1 to n and for all this particular multiplication, we can actually convert this in this particular format.', 'start': 306.655, 'duration': 7.647}], 'summary': 'Converting equation to solve a classification problem with n features.', 'duration': 36.196, 'max_score': 278.106, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jS1CKhALUBQ/pics/jS1CKhALUBQ278106.jpg'}, {'end': 391.978, 'src': 'heatmap', 'start': 332.851, 'weight': 0.765, 'content': [{'end': 340.136, 'text': 'So this we can consider it as a constant and for all this right this top value will be directly proportional to this.', 'start': 332.851, 'duration': 7.285}, {'end': 346.539, 'text': 'You can see that this all value that we are computing this will be directly proportional to this particular equation that I have written over here.', 'start': 340.436, 'duration': 6.103}, {'end': 348.861, 'text': 'Right It will be directly proportional.', 'start': 347.32, 'duration': 1.541}, {'end': 356.167, 'text': 'Right now since it is directly proportional in order to find out the output of this particular value.', 'start': 349.844, 'duration': 6.323}, {'end': 365.871, 'text': 'Right. We need to take the arg max arg max of whatever computation we are actually doing with respect to probability of y,', 'start': 356.887, 'duration': 8.984}, {'end': 368.112, 'text': 'multiplied by probability of all the features.', 'start': 365.871, 'duration': 2.241}, {'end': 370.922, 'text': 'Right We need to take the argmax.', 'start': 369.301, 'duration': 1.621}, {'end': 374.825, 'text': 'argmax basically means that which will be giving us the highest probability.', 'start': 371.443, 'duration': 3.382}, {'end': 376.346, 'text': 'We need to consider that.', 'start': 375.206, 'duration': 1.14}, {'end': 381.37, 'text': 'Suppose for yes it is giving us 0.7 for no it is giving us 0.3.', 'start': 376.747, 'duration': 4.623}, {'end': 389.617, 'text': "Then in this case I'll be considering 0.7 and the output of that particular record that particular feature will be the yes for that particular stuff.", 'start': 381.37, 'duration': 8.247}, {'end': 391.978, 'text': 'So the output for this will be yes.', 'start': 390.517, 'duration': 1.461}], 'summary': 'The output is directly proportional to the computed values, and the highest probability determines the final output.', 'duration': 59.127, 'max_score': 332.851, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jS1CKhALUBQ/pics/jS1CKhALUBQ332851.jpg'}, {'end': 381.37, 'src': 'embed', 'start': 356.887, 'weight': 2, 'content': [{'end': 365.871, 'text': 'Right. We need to take the arg max arg max of whatever computation we are actually doing with respect to probability of y,', 'start': 356.887, 'duration': 8.984}, {'end': 368.112, 'text': 'multiplied by probability of all the features.', 'start': 365.871, 'duration': 2.241}, {'end': 370.922, 'text': 'Right We need to take the argmax.', 'start': 369.301, 'duration': 1.621}, {'end': 374.825, 'text': 'argmax basically means that which will be giving us the highest probability.', 'start': 371.443, 'duration': 3.382}, {'end': 376.346, 'text': 'We need to consider that.', 'start': 375.206, 'duration': 1.14}, {'end': 381.37, 'text': 'Suppose for yes it is giving us 0.7 for no it is giving us 0.3.', 'start': 376.747, 'duration': 4.623}], 'summary': 'In machine learning, we need to find the argmax to determine the highest probability for a given computation and features.', 'duration': 24.483, 'max_score': 356.887, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jS1CKhALUBQ/pics/jS1CKhALUBQ356887.jpg'}], 'start': 68.071, 'title': 'Machine learning features and probability', 'summary': 'Covers understanding features and dependent output in machine learning, computation of probability for a binary classification problem, and techniques for determining the output with the highest probability.', 'chapters': [{'end': 158.679, 'start': 68.071, 'title': 'Understanding machine learning features', 'summary': 'Discusses the concept of features and dependent output in machine learning, explaining how to compute the value y for a classification problem and the need to adjust the base theorem formula based on the dataset.', 'duration': 90.608, 'highlights': ['The chapter explains the concept of features and dependent output in machine learning, emphasizing the need to adjust the base theorem formula based on the dataset and how to compute the value Y for a classification problem.', 'It highlights the example of categorizing a person as obese or not obese based on features like height and weight, illustrating the application of features in a classification problem.']}, {'end': 417.313, 'start': 159.54, 'title': 'Probability and classification in machine learning', 'summary': 'Explains the computation of probability of y given a set of features x1 to xn, for solving a binary classification problem by using multiplication of conditional probabilities and taking argmax to determine the output with the highest probability.', 'duration': 257.773, 'highlights': ['The chapter explains the computation of probability of Y given a set of features X1 to Xn, for solving a binary classification problem by using multiplication of conditional probabilities and taking argmax to determine the output with the highest probability.', 'The equation is converted to represent the probability of Y given the features as a multiplication problem, utilizing pi notation and considering the constant value for all records.', "Taking argmax of the computation involving the probability of Y and the multiplication of conditional probabilities helps in determining the output with the highest probability, enabling the classification of the features into 'yes' or 'no' based on the calculated probabilities."]}], 'duration': 349.242, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jS1CKhALUBQ/pics/jS1CKhALUBQ68071.jpg', 'highlights': ['The chapter explains the computation of probability of Y given a set of features X1 to Xn, for solving a binary classification problem by using multiplication of conditional probabilities and taking argmax to determine the output with the highest probability.', 'The equation is converted to represent the probability of Y given the features as a multiplication problem, utilizing pi notation and considering the constant value for all records.', "Taking argmax of the computation involving the probability of Y and the multiplication of conditional probabilities helps in determining the output with the highest probability, enabling the classification of the features into 'yes' or 'no' based on the calculated probabilities.", 'The chapter explains the concept of features and dependent output in machine learning, emphasizing the need to adjust the base theorem formula based on the dataset and how to compute the value Y for a classification problem.', 'It highlights the example of categorizing a person as obese or not obese based on features like height and weight, illustrating the application of features in a classification problem.']}, {'end': 953.542, 'segs': [{'end': 610.137, 'src': 'embed', 'start': 538.028, 'weight': 0, 'content': [{'end': 540.07, 'text': 'the probability of yes will be 2 by 9.', 'start': 538.028, 'duration': 2.042}, {'end': 544.094, 'text': 'similarly, when the feature will be having overcast, the probability will be 4 by 9.', 'start': 540.07, 'duration': 4.024}, {'end': 548.337, 'text': 'okay, now, This is the data set for outlook and temperature.', 'start': 544.094, 'duration': 4.243}, {'end': 552.5, 'text': 'We have calculated the probability and this also happens internally in the algorithm.', 'start': 548.377, 'duration': 4.123}, {'end': 556.822, 'text': 'The probability of each and every value with respect to the feature is getting calculated.', 'start': 552.92, 'duration': 3.902}, {'end': 559.124, 'text': 'Now suppose I have a data set.', 'start': 557.203, 'duration': 1.921}, {'end': 565.477, 'text': 'I have a data set which is like today in today feature, we have sunny and hot.', 'start': 560.254, 'duration': 5.223}, {'end': 570.799, 'text': 'now, considering this particular data and remember, guys, this is my final yes or no probability.', 'start': 565.477, 'duration': 5.322}, {'end': 578.343, 'text': 'okay, whether the person is going to play or not, and with respect to yes, they are nine records, which is saying yes nine times.', 'start': 570.799, 'duration': 7.544}, {'end': 582.986, 'text': 'so the probability will be nine by fourteen, because the no number of records are five.', 'start': 578.343, 'duration': 4.643}, {'end': 584.726, 'text': 'so nine plus five is fourteen.', 'start': 582.986, 'duration': 1.74}, {'end': 588.009, 'text': 'so you can see that five by fourteen is the probability of no, OK.', 'start': 584.726, 'duration': 3.283}, {'end': 594.863, 'text': 'Now suppose if I have today a scenario where my outlook is sunny and my temperature is hot.', 'start': 588.269, 'duration': 6.594}, {'end': 597.909, 'text': 'We need to determine whether the person is going to play or not.', 'start': 595.083, 'duration': 2.826}, {'end': 606.473, 'text': 'Now, remember, with respect to bias theorem, the formula looks something like this.', 'start': 598.925, 'duration': 7.548}, {'end': 610.137, 'text': 'Probability of yes for today, given today.', 'start': 606.914, 'duration': 3.223}], 'summary': "Using probability calculations, the likelihood of playing based on weather features is determined, with 9 out of 14 records indicating a 'yes' probability.", 'duration': 72.109, 'max_score': 538.028, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jS1CKhALUBQ/pics/jS1CKhALUBQ538028.jpg'}, {'end': 763.302, 'src': 'embed', 'start': 737.452, 'weight': 2, 'content': [{'end': 742.374, 'text': "Given today, I'm getting this particular value as 0.0031..", 'start': 737.452, 'duration': 4.922}, {'end': 748.675, 'text': 'Now the next step, now the very important step is that we also need to find out what is the probability of no given today.', 'start': 742.374, 'duration': 6.301}, {'end': 751.716, 'text': 'And again the equation will change something like this.', 'start': 749.796, 'duration': 1.92}, {'end': 755.018, 'text': 'Probability of Sunny given no.', 'start': 752.056, 'duration': 2.962}, {'end': 757.499, 'text': 'okay, multiplied by probability of hot given no.', 'start': 755.018, 'duration': 2.481}, {'end': 758.219, 'text': 'so given no.', 'start': 757.499, 'duration': 0.72}, {'end': 761.841, 'text': 'basically means I have to go and find out the probability of no when the outlook is sunny.', 'start': 758.219, 'duration': 3.622}, {'end': 763.302, 'text': 'so it is nothing but 3 by 5.', 'start': 761.841, 'duration': 1.461}], 'summary': 'Probability of no given sunny is 3/5 (0.6).', 'duration': 25.85, 'max_score': 737.452, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jS1CKhALUBQ/pics/jS1CKhALUBQ737452.jpg'}, {'end': 847.558, 'src': 'heatmap', 'start': 791.945, 'weight': 0.869, 'content': [{'end': 801.889, 'text': "Now if I want to calculate the probability of yes with respect to this today's condition I have to take 0.031 and I have to normalize it to 1.", 'start': 791.945, 'duration': 9.944}, {'end': 810.563, 'text': 'If I want to normalize it into 1 I just have to do the division of 0.031 plus 0.08571.', 'start': 801.889, 'duration': 8.674}, {'end': 817.286, 'text': "Right. So again again I'm repeating guys, if I want to find out the probability of yes, considering these two particular values, I have to normalize it.", 'start': 810.563, 'duration': 6.723}, {'end': 823.228, 'text': 'Now, if I do this particular computation, guys, this will be somewhere near around 0.27.', 'start': 817.706, 'duration': 5.522}, {'end': 829.531, 'text': 'OK, now you can see that the probability of yes in this scenario, when this today condition is that we are getting it as 0.27.', 'start': 823.228, 'duration': 6.303}, {'end': 835.573, 'text': 'Now, if I want to find out the probability of no, I just have to subtract this 0.27 with this.', 'start': 829.531, 'duration': 6.042}, {'end': 838.314, 'text': 'We will be getting somewhere on 0.73.', 'start': 835.993, 'duration': 2.321}, {'end': 839.315, 'text': 'Now, here you can see that.', 'start': 838.314, 'duration': 1.001}, {'end': 842.396, 'text': 'The probability of no is greater than probability of yes.', 'start': 839.835, 'duration': 2.561}, {'end': 847.558, 'text': 'So in this particular scenario, on this particular condition, the output will actually be no.', 'start': 842.716, 'duration': 4.842}], 'summary': "Probability of 'no' is 0.73, exceeds 'yes' at 0.27.", 'duration': 55.613, 'max_score': 791.945, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jS1CKhALUBQ/pics/jS1CKhALUBQ791945.jpg'}], 'start': 417.894, 'title': 'Bayesian theorem and naive bayes classifier', 'summary': "Covers the application of bayesian theorem in determining the probability of playing tennis based on weather features, utilizing a real-world scenario and dataset, with detailed probability calculations and specific feature values. additionally, it discusses the implementation of naive bayes classifier using a dataset of 14 records to predict the probability of playing tennis based on weather conditions, demonstrating the classifier's process and showcasing a specific prediction with a probability of 0.73.", 'chapters': [{'end': 630.714, 'start': 417.894, 'title': 'Bayesian theorem application', 'summary': 'Explains the application of bayesian theorem in determining the probability of a person playing tennis based on weather features such as outlook and temperature, using a real-world scenario and a dataset, with highlighted details including the probability calculations and the specific values of the features.', 'duration': 212.82, 'highlights': ["The probability of 'yes' when the outlook is sunny is 2 by 9, and the probability of 'no' is 5 by 14, based on the given dataset, which consists of nine 'yes' and five 'no' records.", 'The algorithm internally calculates the probability of each value with respect to the feature, such as outlook and temperature.', 'The chapter introduces a real-world scenario where the outlook is sunny and the temperature is hot, and explains the application of the Bayesian Theorem to determine the probability of a person playing tennis based on these features.']}, {'end': 953.542, 'start': 631.115, 'title': 'Naive bayes classifier', 'summary': "Explains the application of naive bayes classifier using an example with a dataset of 14 records to predict the probability of playing tennis based on the weather conditions, where the output for the given condition of sunny and hot is determined to be 'no' with a probability of 0.73, showcasing the classifier's implementation and normalization process.", 'duration': 322.427, 'highlights': ["The probability of no given today's condition of sunny and hot is calculated to be 0.73, indicating that the output for this condition is 'no'. The probability of no given today's condition of sunny and hot is calculated to be 0.73, showcasing the classifier's implementation and normalization process.", "The probability of yes given today's condition of sunny and hot is determined to be 0.27, showcasing the classifier's implementation and normalization process. The probability of yes given today's condition of sunny and hot is determined to be 0.27, highlighting the implementation and normalization process of the classifier.", 'The probability of yes given sunny and hot is calculated to be 0.0031, showcasing the application of Naive Bayes Classifier with a specific dataset and condition. The probability of yes given sunny and hot is calculated to be 0.0031, demonstrating the application of Naive Bayes Classifier with a specific dataset and condition.']}], 'duration': 535.648, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jS1CKhALUBQ/pics/jS1CKhALUBQ417894.jpg', 'highlights': ['The algorithm internally calculates the probability of each value with respect to the feature, such as outlook and temperature.', 'The chapter introduces a real-world scenario where the outlook is sunny and the temperature is hot, and explains the application of the Bayesian Theorem to determine the probability of a person playing tennis based on these features.', "The probability of no given today's condition of sunny and hot is calculated to be 0.73, indicating that the output for this condition is 'no'.", "The probability of yes given today's condition of sunny and hot is determined to be 0.27, showcasing the classifier's implementation and normalization process.", 'The probability of yes given sunny and hot is calculated to be 0.0031, showcasing the application of Naive Bayes Classifier with a specific dataset and condition.']}], 'highlights': ['The chapter explains the computation of probability of Y given a set of features X1 to Xn, for solving a binary classification problem by using multiplication of conditional probabilities and taking argmax to determine the output with the highest probability.', 'The equation is converted to represent the probability of Y given the features as a multiplication problem, utilizing pi notation and considering the constant value for all records.', 'The algorithm internally calculates the probability of each value with respect to the feature, such as outlook and temperature.', 'The chapter introduces a real-world scenario where the outlook is sunny and the temperature is hot, and explains the application of the Bayesian Theorem to determine the probability of a person playing tennis based on these features.', 'The Nape Bias Classifier is based on Bayes theorem and is used for solving classification problems in machine learning, as mentioned by Krishnayak.', "Bayes theorem is the foundation of the Nape Bias Classifier, and it is derived by conditional probability, explained in Krishnayak's previous tutorial.", "Taking argmax of the computation involving the probability of Y and the multiplication of conditional probabilities helps in determining the output with the highest probability, enabling the classification of the features into 'yes' or 'no' based on the calculated probabilities."]}