Machine Learning Report
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MACHINE LEARNING REPORT
BINARY TREE Binary tree is a tree data structure that categorizes the data. It’s also like a decision tree that decides what is what. ● Sample is the amount of data. ● Value is the number of types of data. ● Class is the answers. MACHINE LEARNING PROCESS
First, we create data in Google Sheets or CSV. Then, we convert the data to numbers, starting from 0. Then, we train the data using sklearn which is a software machine learning library. After training, we run the code and see the prediction using the tree.
TOPIC & DATA For this experiment, our data includes subtypes and measurements. After choosing Desserts as my type, I decided on my answers. My answers are Chocolate Bar, Cookie, and Brownies. Then I chose the measurements based on the desserts’ characteristics: color, shape, and texture. I measured ten of the subtypes by creating random data as shown in the table below.
The numbers next to the words are how I will convert the data to number. The table on the right show how I convert each data to a number. The ones highlighted with pink are the subtypes. The ones highlighted with blue are the first measurement or color. The ones highlighted with yellow are the second measurement or shape. The ones highlighted with green measurement are the third measurement which is texture.
I didn’t face any major mistakes or challenges in this data. Chocolate bar, cookie, and brownies are the same category but they don’t have huge difference or huge similarity. I think I created a good sample data for this experiment test.
ACCURACY The result of this experiment is completely accurate and correct.
The diagram’s first bubble at the top correctly tells how many samples there are, and how many answers are there: it tells that there are 3 different answers and that each answer contains 4, 3, and 3 values each. Then it, defines that there are 4 chocolate bars which is correct. It then defines that there are 6 values that may be cookies, then correctly distinguishes that there are 3 cookies and 3 brownies.
BINARY TREE Binary tree is a tree data structure that categorizes the data. It’s also like a decision tree that decides what is what. ● Sample is the amount of data. ● Value is the number of types of data. ● Class is the answers. MACHINE LEARNING PROCESS
First, we create data in Google Sheets or CSV. Then, we convert the data to numbers, starting from 0. Then, we train the data using sklearn which is a software machine learning library. After training, we run the code and see the prediction using the tree.
TOPIC & DATA For this experiment, our data includes subtypes and measurements. After choosing Desserts as my type, I decided on my answers. My answers are Chocolate Bar, Cookie, and Brownies. Then I chose the measurements based on the desserts’ characteristics: color, shape, and texture. I measured ten of the subtypes by creating random data as shown in the table below.
The numbers next to the words are how I will convert the data to number. The table on the right show how I convert each data to a number. The ones highlighted with pink are the subtypes. The ones highlighted with blue are the first measurement or color. The ones highlighted with yellow are the second measurement or shape. The ones highlighted with green measurement are the third measurement which is texture.
I didn’t face any major mistakes or challenges in this data. Chocolate bar, cookie, and brownies are the same category but they don’t have huge difference or huge similarity. I think I created a good sample data for this experiment test.
ACCURACY The result of this experiment is completely accurate and correct.
The diagram’s first bubble at the top correctly tells how many samples there are, and how many answers are there: it tells that there are 3 different answers and that each answer contains 4, 3, and 3 values each. Then it, defines that there are 4 chocolate bars which is correct. It then defines that there are 6 values that may be cookies, then correctly distinguishes that there are 3 cookies and 3 brownies.
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