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Homework answers / question archive / Introduction Understanding the idea in deciding on a choice in getting a car is basic to everybody particularly the first-time buyer or anyone who is inexperienced in how the car business functions
Introduction Understanding the idea in deciding on a choice in getting a car is basic to everybody particularly the first-time buyer or anyone who is inexperienced in how the car business functions. Generally, we need a car as a method for transportation however as we include fun into it and we tend to forget that we shouldn’t underestimate it. Classifying a good car from a better than average to a terrible one is normally being finished physically with the assistance of a car sales representative who guides us to purchase this along these lines or from the conclusion of our family and companions who had experience with vehicle inconveniences. It would have been better to have a device that can check car features and tell that it’s an X car or a Y car. If there is such a device there should be no worries in purchasing a car. In present times it is continuously the car sales representative who encourages us to purchase this car or not. We may or probably won’t know it consciously however we are ignoring the factors that would help us financially, comfortably, and safely in a long run. The dataset was processed, exploring the relationship of the variables between the attributes and we model the data from different classification models, those are K nearest neighbor and Decision trees in terms of their best set of parameters for each case and performance on car evaluation data set. Methodology Data collection The Car Evaluation Dataset is collected from the UCI Machine learning repository for this assignment. This dataset contains 1727 instances and 6 attributes. Transforming the Variables (Data transformation) When we first load the dataset, few variables may be encoded as data types and they don’t fit well in our dataset for example Classes variable. Target variable) that indicates the Unacceptable, acceptable, good, and very good that only takes the values like 1, 2, 3 and 4 Most of the variables are encoded as object type and in this data analysis all the variable holding categorical variables and the variables are in string format, to go further operation we need to change the String type to integer type, moreover, this models requires the variables to be in integers and we have converted by giving a specified number to each variable (encoding). Train-Test Split Evaluation The train-test split is a technique for evaluating the performance of a machine learning algorithm. The attributes and label/target class were separated into input (X) and output (y) columns, then call the function passing both arrays and have them split appropriately into train and test subsets. Dataset Normalization Normalization refers to rescaling real valued numeric attributes into the range 0 and 1. It is useful to scale the input attributes for a model that relies on the magnitude of values, such as distance measures. y = (x – mean) / standard_deviation Where the mean is calculated as: mean = sum(x) / count(x) And the standard_deviation is calculated as: standard_deviation = sqrt( sum( (x – mean)^2 ) / count(x)) Overview of KNN What are K- Nearest neighbors? a) K- Nearest Neighbors is a supervised machine learning algorithm as target variable is known b) Non-parametric as it does not assume the underlying data distribution pattern c) Lazy algorithm as KNN does not have a training step. All data points will be used only at the time of prediction. With no training step, the prediction step is costly. An eager learner algorithm eagerly learns during the training step. d) Used for both Classification and Regression e) Uses feature similarity to predict the cluster that the new point will fall into. What is K is K nearest neighbors? K is a number used to identify similar neighbors for the new data point. Referring to our example of a friend circle in our new neighborhood. We select 3 neighbors that we want to be very close friends with based on common thinking or hobbies. In this case, K is 3. KNN takes K nearest neighbors to decide where the new data point belongs to. This decision is based on feature similarity. How do we choose the value of K? Choice of K has a drastic impact on the results we obtain from KNN. The regularization method was used checking, which takes values range between 1 and 51 and checks the accuracy score for all values of k in this range and we then use this to determine the best value of k. We can take the test set and plot the accuracy rate or F1 score against different values of K. We see a high error rate for the test set when K=1. Hence we can conclude that model over fits when k=1. For a high value of K, we see that the F1 score starts to drop. The test set reaches a minimum error rate when k=5. This is very similar to the elbow method used in K-means. What is a Confusion Matrix? A Confusion matrix is an N x N matrix used for evaluating the performance of a classification model, where N is the number of target classes. The matrix compares the actual target values with those predicted by the machine learning model. This gives us a holistic view of how well our classification model is performing and what kinds of errors it is making. A classification report is about key metrics in a classification problem. You'll have precision, recall, f1-score, and support for each class you're trying to find. ? ? ? ? The recall means "how many of this class you find over the whole number of elements of this class" The precision will be "how many are correctly classified among that class" The f1-score is the harmonic mean between precision & recall The support is the number of occurrences of the given class in your dataset. Introduction Understanding the idea in deciding on a choice in getting a car is basic to everybody particularly the first-time buyer or anyone who is inexperienced in how the car business functions. Generally, we need a car as a method for transportation however as we include fun into it and we tend to forget that we shouldn’t underestimate it. Classifying a good car from a better than average to a terrible one is normally being finished physically with the assistance of a car sales representative who guides us to purchase this along these lines or from the conclusion of our family and companions who had experience with vehicle inconveniences. It would have been better to have a device that can check car features and tell that it’s an X car or a Y car. If there is such a device there should be no worries in purchasing a car. In present times it is continuously the car sales representative who encourages us to purchase this car or not. We may or probably won’t know it consciously however we are ignoring the factors that would help us financially, comfortably, and safely in a long run. The dataset was processed, exploring the relationship of the variables between the attributes and we model the data from different classification models, those are K nearest neighbor and Decision trees in terms of their best set of parameters for each case and performance on car evaluation data set. Methodology Data collection The Car Evaluation Dataset is collected from the UCI Machine learning repository for this assignment. This dataset contains 1727 instances and 6 attributes. Transforming the Variables (Data transformation) When we first load the dataset, few variables may be encoded as data types and they don’t fit well in our dataset for example Classes variable. Target variable) that indicates the Unacceptable, acceptable, good, and very good that only takes the values like 1, 2, 3 and 4 Most of the variables are encoded as object type and in this data analysis all the variable holding categorical variables and the variables are in string format, to go further operation we need to change the String type to integer type, moreover, this models requires the variables to be in integers and we have converted by giving a specified number to each variable (encoding). Train-Test Split Evaluation The train-test split is a technique for evaluating the performance of a machine learning algorithm. The attributes and label/target class were separated into input (X) and output (y) columns, then call the function passing both arrays and have them split appropriately into train and test subsets. Dataset Normalization Normalization refers to rescaling real valued numeric attributes into the range 0 and 1. It is useful to scale the input attributes for a model that relies on the magnitude of values, such as distance measures. y = (x – mean) / standard_deviation Where the mean is calculated as: mean = sum(x) / count(x) And the standard_deviation is calculated as: standard_deviation = sqrt( sum( (x – mean)^2 ) / count(x)) Overview of KNN What are K- Nearest neighbors? a) K- Nearest Neighbors is a supervised machine learning algorithm as target variable is known b) Non-parametric as it does not assume the underlying data distribution pattern c) Lazy algorithm as KNN does not have a training step. All data points will be used only at the time of prediction. With no training step, the prediction step is costly. An eager learner algorithm eagerly learns during the training step. d) Used for both Classification and Regression e) Uses feature similarity to predict the cluster that the new point will fall into. What is K is K nearest neighbors? K is a number used to identify similar neighbors for the new data point. Referring to our example of a friend circle in our new neighborhood. We select 3 neighbors that we want to be very close friends with based on common thinking or hobbies. In this case, K is 3. KNN takes K nearest neighbors to decide where the new data point belongs to. This decision is based on feature similarity. How do we choose the value of K? Choice of K has a drastic impact on the results we obtain from KNN. The regularization method was used checking, which takes values range between 1 and 51 and checks the accuracy score for all values of k in this range and we then use this to determine the best value of k. We can take the test set and plot the accuracy rate or F1 score against different values of K. We see a high error rate for the test set when K=1. Hence we can conclude that model over fits when k=1. For a high value of K, we see that the F1 score starts to drop. The test set reaches a minimum error rate when k=5. This is very similar to the elbow method used in K-means. What is a Confusion Matrix? A Confusion matrix is an N x N matrix used for evaluating the performance of a classification model, where N is the number of target classes. The matrix compares the actual target values with those predicted by the machine learning model. This gives us a holistic view of how well our classification model is performing and what kinds of errors it is making. A classification report is about key metrics in a classification problem. You'll have precision, recall, f1-score, and support for each class you're trying to find. ? ? ? ? The recall means "how many of this class you find over the whole number of elements of this class" The precision will be "how many are correctly classified among that class" The f1-score is the harmonic mean between precision & recall The support is the number of occurrences of the given class in your dataset. Introduction Understanding the idea in deciding on a choice in getting a car is basic to everybody particularly the first-time buyer or anyone who is inexperienced in how the car business functions. Generally, we need a car as a method for transportation however as we include fun into it and we tend to forget that we shouldn’t underestimate it. Classifying a good car from a better than average to a terrible one is normally being finished physically with the assistance of a car sales representative who guides us to purchase this along these lines or from the conclusion of our family and companions who had experience with vehicle inconveniences. It would have been better to have a device that can check car features and tell that it’s an X car or a Y car. If there is such a device there should be no worries in purchasing a car. In present times it is continuously the car sales representative who encourages us to purchase this car or not. We may or probably won’t know it consciously however we are ignoring the factors that would help us financially, comfortably, and safely in a long run. The dataset was processed, exploring the relationship of the variables between the attributes and we model the data from different classification models, those are K nearest neighbor and Decision trees in terms of their best set of parameters for each case and performance on car evaluation data set. Methodology Data collection The Car Evaluation Dataset is collected from the UCI Machine learning repository for this assignment. This dataset contains 1727 instances and 6 attributes. Transforming the Variables (Data transformation) When we first load the dataset, few variables may be encoded as data types and they don’t fit well in our dataset for example Classes variable. Target variable) that indicates the Unacceptable, acceptable, good, and very good that only takes the values like 1, 2, 3 and 4 Most of the variables are encoded as object type and in this data analysis all the variable holding categorical variables and the variables are in string format, to go further operation we need to change the String type to integer type, moreover, this models requires the variables to be in integers and we have converted by giving a specified number to each variable (encoding). Train-Test Split Evaluation The train-test split is a technique for evaluating the performance of a machine learning algorithm. The attributes and label/target class were separated into input (X) and output (y) columns, then call the function passing both arrays and have them split appropriately into train and test subsets. Dataset Normalization Normalization refers to rescaling real valued numeric attributes into the range 0 and 1. It is useful to scale the input attributes for a model that relies on the magnitude of values, such as distance measures. y = (x – mean) / standard_deviation Where the mean is calculated as: mean = sum(x) / count(x) And the standard_deviation is calculated as: standard_deviation = sqrt( sum( (x – mean)^2 ) / count(x)) Overview of KNN What are K- Nearest neighbors? a) K- Nearest Neighbors is a supervised machine learning algorithm as target variable is known b) Non-parametric as it does not assume the underlying data distribution pattern c) Lazy algorithm as KNN does not have a training step. All data points will be used only at the time of prediction. With no training step, the prediction step is costly. An eager learner algorithm eagerly learns during the training step. d) Used for both Classification and Regression e) Uses feature similarity to predict the cluster that the new point will fall into. What is K is K nearest neighbors? K is a number used to identify similar neighbors for the new data point. Referring to our example of a friend circle in our new neighborhood. We select 3 neighbors that we want to be very close friends with based on common thinking or hobbies. In this case, K is 3. KNN takes K nearest neighbors to decide where the new data point belongs to. This decision is based on feature similarity. How do we choose the value of K? Choice of K has a drastic impact on the results we obtain from KNN. The regularization method was used checking, which takes values range between 1 and 51 and checks the accuracy score for all values of k in this range and we then use this to determine the best value of k. We can take the test set and plot the accuracy rate or F1 score against different values of K. We see a high error rate for the test set when K=1. Hence we can conclude that model over fits when k=1. For a high value of K, we see that the F1 score starts to drop. The test set reaches a minimum error rate when k=5. This is very similar to the elbow method used in K-means. What is a Confusion Matrix? A Confusion matrix is an N x N matrix used for evaluating the performance of a classification model, where N is the number of target classes. The matrix compares the actual target values with those predicted by the machine learning model. This gives us a holistic view of how well our classification model is performing and what kinds of errors it is making. A classification report is about key metrics in a classification problem. You'll have precision, recall, f1-score, and support for each class you're trying to find. ? ? ? ? The recall means "how many of this class you find over the whole number of elements of this class" The precision will be "how many are correctly classified among that class" The f1-score is the harmonic mean between precision & recall The support is the number of occurrences of the given class in your dataset.
