Your task is to rewrite a given candidate prompt into a better prompt copying all the provided values in the rewritten better prompt. A better prompt is specific, non-ambiguous, complete, and specifies the format structure to easily extract the final answer. You have been given the following set of examples where each example consists of 1) the candidate prompt (the bad prompt which needs to be written into better or good prompt). 2) the Reason highlighting the shortcomings of the candidate prompt. This is useful so first identify the key weakness in the bad prompt and then rewrite the better version. 3) Better Prompt Type: Helps in identifying the type of better prompt. This can help reduce the search space for each prompt type. Remember the better prompt should not alter or skip the information provided in the candidate prompt. Once you have understood the examples below, please provide a reason and rewrite the candidate prompt into a better one which is given at the end. Remember to provide a common output format like "the answer is \\answer{}" whenever required for example for questions requiring mathematical or logical reasoning.

###Candidate Prompt###
Given a pandas DataFrame [INPUT], write a Python function that trains a classifier from the sklearn library. The DataFrame has a 'target' column and other feature columns. The function signature should be: `def train_classifier(df: pd.DataFrame) -> Classifier:`
###Reason###
The candidate prompt is vague about the type of classifier to be used and does not provide any parameters for splitting the data or the classifier. This ambiguity might lead the GPT model to guess the user's intentions, which may not align with their actual needs. On the other hand, the better prompt gives specific, clear, and complete instructions. It specifies the classifier type, split ratio, random states, and parameters for the classifier, eliminating any room for guesswork and ensuring the GPT-based model generates the desired output.
###Better Prompt###
Given a pandas DataFrame, write a Python function that trains a Random Forest Classifier from the sklearn library. The DataFrame will have a 'target' column for the labels, and the remaining columns are features. Split the data into training and testing sets with a ratio of 80:20 using the `train_test_split` function from sklearn with a random state of 42. Use the RandomForestClassifier with `n_estimators` set to 100, `max_depth` set to 2, `random_state` set to 0. The function signature should be: `def train_rf_classifier(df: pd.DataFrame) -> RandomForestClassifier:`

###Candidate Prompt###
Andy is a lawyer who's working on two lawsuits. The first lawsuit has a 30% chance of paying out $5,000,000 upon a win and $0 if he loses it. The second lawsuit has a 50% chance of paying out $1,000,000 if Andy loses and a 50% chance of paying out $2,000,000 if he wins.  Expressed as a percentage, how much more likely is it that Andy loses both lawsuits compared to winning both of them?
###Reason###
The candidate prompt asks for a percentage comparison which could be subject to multiple interpretations - it could be asking for a relative percent increase, percent of percent, or just the difference in probability percentages. Furthermore, the prompt doesn't provide a common output format for the answer which will provide a consistent and easy extraction of the final answer. The better prompt clarifies the required calculations and provides a common output format for the answer. The better prompt should not alter or skip the information provided in the candidate prompt which could lead to incorrect calculation.
###Better Prompt###
Andy is a lawyer who's working on two lawsuits. The first lawsuit has a 30% chance of paying out $5,000,000 upon a win and $0 if he loses it. The second lawsuit has a 50% chance of paying out $1,000,000 if Andy loses and a 50% chance of paying out $2,000,000 if he wins. Calculate the difference in the probabilities of Andy losing both lawsuits and winning both lawsuits, expressed as a percentage. Subtract the probability of winning both lawsuits from the probability of losing both lawsuits. Print the output in the format "The answer is \\answer{}".

###Candidate Prompt###
Jim decides to open up a bike shop.  The most common repair he does is fixing bike tires.  He charges $20 for this and it cost him $5 in parts.  In one month Jim does 300 of these repairs.  He also does 2 more complex repairs for $300 each and cost $50 in parts.  He also sells $2000 profit worth of things from his retail shop.  If rent and other fixed expense for the shop is $4000 a month how much profit did the shop make?
###Reason###
The candidate prompt, in this case, involves the model attempting to solve the entire problem at once. While this may work for simpler tasks, in complex scenarios it might lead to less accurate or nonsensical outputs. Breaking the problem down would make the reasoning more explicit and easier for the model to handle. The 'Chain of Thought' approach is better in this context because it makes the computation more manageable by breaking it down into simpler steps. This approach can be particularly useful for complex mathematical problems as it allows the model to focus on one part of the problem at a time. It also provides a clear and organized structure, making it easier for the user to follow the model's reasoning process. The candidate prompt also does not provide a common output format like "the answer is \\answer{}". The better prompt should not alter or skip the information provided in the candidate prompt which could lead to incorrect calculation.
###Better Prompt###
Solve the following problem step by step
Step 1: Calculate the profit earned by Jim from fixing bike tires given that he charges $20 and it cost him $5 in parts after doing 300 of these repairs. Step 2: Compute the profit from the two more complex repairs, which cost $50 in parts and are charged at $300 each. Step 3: Sum up the profit from the fixed bike tires, the complex repairs, and the $2000 profit from the retail shop. Step 4: Subtract the rent and other fixed expenses which amounts to $4000 a month, from the total profit to find out how much profit Jim's shop made. Print the output in the format "The answer is \\answer{}"

###Candidate Prompt###
A car travels at a speed of 60 miles per hour. How far will it travel in 2.5 hours?
###Reason###
By using chain of thoughts prompting, we can guide the model to break down the word problem into logical steps and provide a step-by-step solution. The better prompt should not alter or skip the information provided in the candidate prompt which could lead to incorrect calculation.
###Better Prompt###
Let's solve the following word problem step by step: A car travels at a speed of 60 miles per hour. How far will it travel in 2.5 hours? Start by multiplying the speed (60 mph) by the time (2.5 hours) to find the total distance. Then, apply the formula Distance = Speed × Time to calculate the answer. Print the output in the format "The answer is \\answer{}".

###Candidate Prompt###
You have to generate a question for the given text. The question should be relevant and contextually appropriate, capturing the essence of the text.
###Reason###
The candidate prompt is relatively short and lacks specific instructions and requirements for generating a question for the text. Although it mentions the need for a relevant and contextually appropriate question that captures the essence of the text, it does not provide clear guidelines on how to determine the essence, which question generation model to use, or how to handle complex or technical text. The prompt is vague and leaves many crucial details to interpretation, making it difficult for the model to generate an accurate question.
###Better Prompt###
Your task is to generate a relevant and contextually appropriate question for the given text that captures the essence of the text. Use a well-defined question generation model or library to generate the question. Pay attention to the nuances of the text, such as the main ideas, arguments, or themes, that may affect the question generation. Ensure that the question is contextually appropriate and maintains the same intent as the original text. Provide a clear and concise question that accurately reflects the essence of the text. Aim for a high level of accuracy and coherence in your question generation. If necessary, provide a brief explanation or evidence to support your question.

###Candidate Prompt###
The concert was scheduled to be on 06/01/1943, but was delayed by one day to today. What is the date 10 days ago in MM/DD/YYYY? 
###Reason###
The candidate prompt is relatively short and lacks specific instructions and requirements for solving the problem. Although it mentions the need to find the date 10 days ago in MM/DD/YYYY format, it does not provide clear guidelines on how to calculate the date, which calendar system to use, or how to handle leap years. The prompt is vague and leaves many crucial details to interpretation, making it difficult for the model to generate an accurate answer. The candidate prompt also does not provide a common output format like "the answer is \\answer{}".
###Better Prompt###
Your task is to calculate the date 10 days ago in MM/DD/YYYY format, given that the concert was scheduled to be on 06/01/1943 but was delayed by one day to today. Use the Gregorian calendar system to calculate the date, taking into account leap years and the number of days in each month. Pay attention to the nuances of the problem, such as the initial date and the number of days to subtract, that may affect the calculation. Ensure that the date is contextually appropriate and maintains the same intent as the original problem. Provide a clear and concise date that accurately reflects the solution to the problem. Aim for a high level of accuracy and consistency in your calculations. If necessary, provide a brief explanation or evidence to support your answer. Print the output in the format "The answer is \\answer{}"

###Candidate Prompt###
Is the following sentence plausible? "Joao Moutinho caught the screen pass in the NFC championship."
###Reason###
The candidate prompt is relatively short and lacks specific instructions and requirements for evaluating the plausibility of the sentence. Although it provides an example and a correct answer, it does not provide clear guidelines on how to reason through the sentence or how to handle ambiguous or complex sentences. The prompt is vague and leaves many crucial details to interpretation, making it difficult for the model to generate an accurate answer. The candidate prompt also does not provide a common output format like "the answer is \\answer{}".
###Better Prompt###
Your task is to determine the plausibility of the following sentence: "Joao Moutinho caught the screen pass in the NFC championship." Provide a clear and concise answer, along with a brief explanation or evidence to support your answer. Consider the context of the sentence, such as the teams, players, and events mentioned, as well as the rules and conventions of the sport. Ensure that your answer is contextually appropriate and maintains the same intent as the original sentence. Pay attention to providing a well-reasoned and evidence-based answer that is easy to understand and follow. Print the output in the format "The answer is \\answer{}"

###Candidate Prompt###
Paraphrase the given sentences. Make sure the paraphrased versions have the same meaning as the original sentences.
###Reason###
The candidate prompt is relatively short and lacks specific instructions and requirements for paraphrasing. It only mentions the need for paraphrasing and maintaining the same meaning as the original sentences. However, it does not provide clear guidelines on how to ensure variations in wording, sentence structure, or phrasing. The prompt is vague and leaves many crucial details to interpretation, making it difficult for the model to generate accurate and diverse paraphrases.
###Better Prompt###
Your task is to paraphrase the given sentences while maintaining the original meaning. The paraphrased versions should exhibit variations in wording, sentence structure, and phrasing. Aim for paraphrases that are linguistically diverse, capturing different ways of expressing the same ideas. Ensure that the paraphrases are contextually appropriate and maintain the same intent as the original sentences. Pay attention to preserving the nuances, tone, and style of the original sentences during the paraphrasing process.

###Candidate Prompt###
Apply a function to the final input list to generate the output list. Use any preceding inputs and outputs as examples.
input : [1, 7, 9, 4, 6, 2, 0]
output : [0]
input : [8, 3, 4, 0, 5, 1, 6, 9, 2]
output : [6]
input : [8, 4, 5, 9, 0, 6, 1]
output : [1]
###Reason###
The candidate prompt says nothing about the task at hand which makes it ambiguous. Since there could be many interpretations from a few input-output pairs the prompt will lead the model to guess the task at hand and we may get different outputs. To tackle this problem, the better prompt should specify the task at hand by clearly specifying the instructions. Specifying the objective function will make the better prompt unambiguous and the model would not guess. Also, remember we need to consider all the edge cases (such as zero or one element) before designing a better prompt.
###Better Prompt###
From the given list as Input, remove all the elements from the input list except the 7th element. Print the output in the format "The answer is \\answer{}".
input : [1, 7, 9, 4, 6, 2, 0]
output : [0]
input : [8, 3, 4, 0, 5, 1, 6, 9, 2]
output : [6]
input : [8, 4, 5, 9, 0, 6, 1]
output : [1]
input : [3, 1, 7, 2, 9, 6, 8, 4, 5, 0]
output : [8]

###Candidate Prompt###
Input: Please call once you get there.
Output: Please call upon your arrival.
Input: Thanks for your help.
Output: Your assistance is much appreciated.
Input: This song is fire.
Output: This song is incredibly good.
###Reason###
The candidate prompt is vague, ambiguous, and incomplete. Merely giving input and output pairs makes the interpretation of the task ambiguous. There could be several transformation functions that could be applied to obtain the same input and output pairs. The better prompt should analyze the examples, identify the transformation function, and make the task clear while generating the better prompt type. Specifying the objective function will make the better prompt unambiguous and the model would not guess. Also, remember we need to consider all the edge cases (such as zero or one element) before designing a better prompt.  
###Better Prompt###
Write a paraphrase of the input sentence, but use a formal style. Print the output in the format "The answer is \\answer{}"
Input: Please call once you get there.
Output: Please call upon your arrival.
Input: Thanks for your help.
Output: Your assistance is much appreciated.
Input: This song is fire.
Output: This song is incredibly good.

###Candidate Prompt###