Multilabel Classification in Machine Learning
Hello everyone… I found a dataset not long ago from Alexey Grigorev free ML course, it’s pretty interesting check out the course here , and the link to the dataset...
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Heyy, Today I’ll show you a basic heart disease detection model (a model that can detect if a person has heart disease or not), using a Linear Classifier model.
Basically, a LinearClassifier is a model that uses stats to determine the group or class an object belongs to. the model makes a prediction based on the linear combination of the object characteristics. graph representation
There a lot more to CNNs you can read more on wikipedia
Lets get to the implementation now:
lets import the libraries:
#import libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from IPython.display import clear_output
from six.moves import urllib
import tensorflow.compat.v2.feature_column as fc
import tensorflow as tf
alright we are good to go, lets load the data and preprocess:
#read data and preprocess
df_train = pd.read_csv('/content/train1.csv')
df_eval = pd.read_csv('/content/eval1.csv')
y_train = df_train.pop('HeartDisease')
y_eval = df_eval.pop('HeartDisease')
#divide different columns with respect to their data_type
CATEGORICAL_COLUMNS = ['Sex', 'ChestPainType', 'RestingBP', 'Cholesterol', 'FastingBS', 'RestingECG', 'MaxHR', 'ExerciseAngina', 'ST_Slope']
NUMERIC_COLUMNS = ['Age', 'Oldpeak']
feature_columns = []
for feature_name in CATEGORICAL_COLUMNS:
vocabulary = df_train[feature_name].unique() #print all the unique values in each columns
feature_columns.append(tf.feature_column.categorical_column_with_vocabulary_list(feature_name, vocabulary))
for feature_name in NUMERIC_COLUMNS:
feature_columns.append(tf.feature_column.numeric_column(feature_name, dtype= tf.float64))
“tf.feature_column.categorical_column_with_vocabulary_list(feature_name, vocabulary)”
basically this function creates a categorical column with a list of possible values. while a feature column is a list that is used to specify how Tensors received from the input should be transformed and comined before entering the model
ok. Let’s move on
next we would create an input function that helps with training the model in batches and epoches
def make_input_function(data_df, label_df, num_epochs=10, shuffle=True, batch_size=32):
def input(): # this function will be returned in the function
data_set = tf.data.Dataset.from_tensor_slices((dict(data_df), label_df))
if shuffle:
data_set = data_set.shuffle(1000) #randomize the order of data
data_set = data_set.batch(batch_size).repeat(num_epochs) #split the data into sets(batches) of 32 and repeat the process 10 times(num of epochs)
return data_set #returns a batch of the data_set
return input
train_input_function = make_input_function(df_train, y_train) #change the format of the dataset into one that we can feed the model
eval_input_function = make_input_function(df_eval, y_eval, num_epochs= 1, shuffle= False) #this is for testing the model
So with this function we can successfully feed the model with the correct format of data and labels.
The next step is to initialize the model, train and test the model for accuracy
#initialization of the model with tensorflow estimator
linear_estimator = tf.estimator.LinearClassifier(feature_columns= feature_columns)
linear_estimator.train(train_input_function) #trainning
result = linear_estimator.evaluate(eval_input_function) #testing
clear_output() #clear console outputs
print(result['accuracy']) #this will show the accuracy of the model's prediction on the data_set for evaluatio
#this will vary since the testing data_set is on shuffle
you can get the source code at my github page: click here to visit
If you have any issue feel free to contact me (use the contact me box below 👀). I’ll definetly try to help you ✨✨
And if you have any project idea or you wanna colaborate with me don’t hesitate I’ll always want to join your team 😌
Allright… That’s all for now. Byee 👋🏿
